Dissertations / Theses on the topic 'Surveillance video analysis'
To see the other types of publications on this topic, follow the link: Surveillance video analysis.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Surveillance video analysis.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Bales, Michael Ryan. "Illumination compensation in video surveillance analysis." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39535.
Full textAbstract:
Problems in automated video surveillance analysis caused by illumination changes are explored, and solutions are presented. Controlled experiments are first conducted to measure the responses of color targets to changes in lighting intensity and spectrum. Surfaces of dissimilar color are found to respond significantly differently. Illumination compensation model error is reduced by 70% to 80% by individually optimizing model parameters for each distinct color region, and applying a model tuned for one region to a chromatically different region increases error by a factor of 15. A background model--called BigBackground--is presented to extract large, stable, chromatically self-similar background features by identifying the dominant colors in a scene. The stability and chromatic diversity of these features make them useful reference points for quantifying illumination changes. The model is observed to cover as much as 90% of a scene, and pixels belonging to the model are 20% more stable on average than non-member pixels. Several illumination compensation techniques are developed to exploit BigBackground, and are compared with several compensation techniques from the literature. Techniques are compared in terms of foreground / background classification, and are applied to an object tracking pipeline with kinematic and appearance-based correspondence mechanisms. Compared with other techniques, BigBackground-based techniques improve foreground classification by 25% to 43%, improve tracking accuracy by an average of 20%, and better preserve object appearance for appearance-based trackers. All algorithms are implemented in C or C++ to support the consideration of runtime performance. In terms of execution speed, the BigBackground-based illumination compensation technique is measured to run on par with the simplest compensation technique used for comparison, and consistently achieves twice the frame rate of the two next-fastest techniques.
2
Li, Hao. "Advanced video analysis for surveillance applications." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555815.
Full textAbstract:
This thesis addresses the issues of applying advanced video analytics for surveillance applications. A video surveillance system can be defined as a technological tool that assists humans by providing an extended perception and capability of capturing interesting activities in the monitored scene. The prime components of video surveillance systems include moving object detection, object tracking, and anomaly detection. Moving object detection extracts the foreground silhouettes of moving objects. The object tracking component then applies the foreground information to create correspondences between tracks in the previous frame and objects in the current frame. The most challenging part of the system concerns the use of extracted scene information from the moving objects and object tracking for anomaly detection. The thesis proposes novel approaches for each of the main components above. They include: 1) an efficient foreground detection algorithm based on block-based detection and improved pixel-based Gaussian Mixture Model (GMM) refinement that can selectively update pixel information in each image region; 2) an adaptive object tracker that combines the merits of Kalman, mean-shift and particle filtering; 3) a feature clustering algorithm, which can automatically choose the optimal number of clusters in the training data for scene pattern classification; 4) a statistical scene modeller based on Bayesian theory and GMM, which combines object-based and local region-based information for enhanced anomaly detection. In addition, a layered feedback system architecture is proposed for using high- level detection results for improving low-level detection performance. Compared with common open-loop approaches, this increases the system reliability at the expense of using little extra computation. Moreover, considering the capability of real-time operation, robustness, and detection accuracy, which are key factors of video surveillance systems, appropriate trade-offs between complexity and detection performance are introduced in the relevant phases of the system, such as in moving object detection and in object tracking. The performance of the proposed system is evaluated with various video datasets. Both qualitative and quantitative measures are applied, for example visual comparison and precision-recall curves. The proposed moving object detection achieves an average of 52% and 38% improvement in terms of false positive detected pixels compared with a Gaussian Model (GM) and a GMM respectively. The object tracking component reduces the computation by 10% compared to a mean-shift filter while maintaining better tracking results. The proposed anomaly detection algorithm also outperforms previously proposed approaches. These results demonstrate the effectiveness of the proposed video surveillance system framework.
3
Yoon, Kyongil. "Key-frame appearance analysis for video surveillance." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2818.
Full textAbstract:
Thesis (Ph. D.) -- University of Maryland, College Park, 2005.Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
4
Savadatti-Kamath, Sanmati S. "Video analysis and compression for surveillance applications." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26602.
Full textAbstract:
Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.Committee Chair: Dr. J. R. Jackson; Committee Member: Dr. D. Scott; Committee Member: Dr. D. V. Anderson; Committee Member: Dr. P. Vela; Committee Member: Dr. R. Mersereau. Part of the SMARTech Electronic Thesis and Dissertation Collection.
5
Guler, Puren. "Automated Crowd Behavior Analysis For Video Surveillance Applications." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614659/index.pdf.
Full textAbstract:
Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groupspeople counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behaviors of individuals in a crowd (object based) and using this knowledge to make deductions regarding the crowd behavior and analyzing the crowd as a whole (holistic based). In this work, a holistic approach is used to develop a real-time abnormality detection in crowds using scale invariant feature transform (SIFT) based features and unsupervised machine learning techniques.
6
Sutor, S. R. (Stephan R. ). "Large-scale high-performance video surveillance." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205618.
Full textAbstract:
Abstract The last decade was marked by a set of harmful events ranging from economical crises to organized crime, acts of terror and natural catastrophes. This has led to a paradigm transformation concerning security. Millions of surveillance cameras have been deployed, which led to new challenges, as the systems and operations behind those cameras could not cope with the rapid growth in number of video cameras and systems. Looking at today’s control rooms, often hundreds or even thousands of cameras are displayed, overloading security officers with irrelevant information. The purpose of this research was the creation of a novel video surveillance system with automated analysis mechanisms which enable security authorities and their operators to cope with this information flood. By automating the process, video surveillance was transformed into a proactive information system. The progress in technology as well as the ever increasing demand in security have proven to be an enormous driver for security technology research, such as this study. This work shall contribute to the protection of our personal freedom, our lives, our property and our society by aiding the prevention of crime and terrorist attacks that diminish our personal freedom. In this study, design science research methodology was utilized in order to ensure scientific rigor while constructing and evaluating artifacts. The requirements for this research were sought in close cooperation with high-level security authorities and prior research was studied in detail. The created construct, the “Intelligent Video Surveillance System”, is a distributed, highly-scalable software framework, that can function as a basis for any kind of high-performance video surveillance system, from installations focusing on high-availability to flexible cloud-based installation that scale across multiple locations and tens of thousands of cameras. First, in order to provide a strong foundation, a modular, distributed system architecture was created, which was then augmented by a multi-sensor analysis process. Thus, the analysis of data from multiple sources, combining video and other sensors in order to automatically detect critical events, was enabled. Further, an intelligent mobile client, the video surveillance local control, which addressed remote access applications, was created. Finally, a wireless self-contained surveillance system was introduced, a novel smart camera concept that enabled ad hoc and mobile surveillance. The value of the created artifacts was proven by evaluation at two real-world sites: An international airport, which has a large-scale installation with high-security requirements, and a security service provider, offering a multitude of video-based services by operating a video control center with thousands of cameras connectedTiivistelmä Viime vuosikymmen tunnetaan vahingollisista tapahtumista alkaen talouskriiseistä ja ulottuen järjestelmälliseen rikollisuuteen, terrori-iskuihin ja luonnonkatastrofeihin. Tämä tilanne on muuttanut suhtautumista turvallisuuteen. Miljoonia valvontakameroita on otettu käyttöön, mikä on johtanut uusiin haasteisiin, koska kameroihin liittyvät järjestelmät ja toiminnot eivät pysty toimimaan yhdessä lukuisien uusien videokameroiden ja järjestelmien kanssa. Nykyajan valvontahuoneissa voidaan nähdä satojen tai tuhansien kameroiden tuottavan kuvaa ja samalla runsaasti tarpeetonta informaatiota turvallisuusvirkailijoiden katsottavaksi. Tämän tutkimuksen tarkoitus oli luoda uusi videovalvontajärjestelmä, jossa on automaattiset analyysimekanismit, jotka mahdollistavat turva-alan toimijoiden ja niiden operaattoreiden suoriutuvan informaatiotulvasta. Automaattisen videovalvontaprosessin avulla videovalvonta muokattiin proaktiiviseksi tietojärjestelmäksi. Teknologian kehitys ja kasvanut turvallisuusvaatimus osoittautuivat olevan merkittävä ajuri turvallisuusteknologian tutkimukselle, kuten tämä tutkimus oli. Tämä tutkimus hyödyttää yksittäisen ihmisen henkilökohtaista vapautta, elämää ja omaisuutta sekä yhteisöä estämällä rikoksia ja terroristihyökkäyksiä. Tässä tutkimuksessa suunnittelutiedettä sovellettiin varmistamaan tieteellinen kurinalaisuus, kun artefakteja luotiin ja arvioitiin. Tutkimuksen vaatimukset perustuivat läheiseen yhteistyöhön korkeatasoisten turva-alan viranomaisten kanssa, ja lisäksi aiempi tutkimus analysoitiin yksityiskohtaisesti. Luotu artefakti - ’älykäs videovalvontajärjestelmä’ - on hajautettu, skaalautuva ohjelmistoviitekehys, joka voi toimia perustana monenlaiselle huipputehokkaalle videovalvontajärjestelmälle alkaen toteutuksista, jotka keskittyvät saatavuuteen, ja päättyen joustaviin pilviperustaisiin toteutuksiin, jotka skaalautuvat useisiin sijainteihin ja kymmeniin tuhansiin kameroihin. Järjestelmän tukevaksi perustaksi luotiin hajautettu järjestelmäarkkitehtuuri, jota laajennettiin monisensorianalyysiprosessilla. Siten mahdollistettiin monista lähteistä peräisin olevan datan analysointi, videokuvan ja muiden sensorien datan yhdistäminen ja automaattinen kriittisten tapahtumien tunnistaminen. Lisäksi tässä työssä luotiin älykäs kännykkäsovellus, videovalvonnan paikallinen kontrolloija, joka ohjaa sovelluksen etäkäyttöä. Viimeksi tuotettiin langaton itsenäinen valvontajärjestelmä – uudenlainen älykäs kamerakonsepti – joka mahdollistaa ad hoc -tyyppisen ja mobiilin valvonnan. Luotujen artefaktien arvo voitiin todentaa arvioimalla ne kahdessa reaalimaailman ympäristössä: kansainvälinen lentokenttä, jonka laajamittaisessa toteutuksessa on korkeat turvavaatimukset, ja turvallisuuspalveluntuottaja, joka tarjoaa moninaisia videopohjaisia palveluja videovalvontakeskuksen avulla käyttäen tuhansia kameroita
7
Feather, Ryan K. "TRACKING AND ACTIVITY ANALYSIS IN WIDE AREA AERIAL SURVEILLANCE VIDEO." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1313525739.
Full text
8
Youssef, Wael Farid. "Instanciation d'un schéma de description textuel de scènes de vidéo surveillance." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30249.
Full textAbstract:
Les systèmes de vidéosurveillance sont des outils importants pour les agences chargées de l'application de la loi dans la lutte contre la criminalité. Les chambres de contrôle de la vidéosurveillance ont deux fonctions principales : surveiller en direct les zones de surveillance et résoudre les infractions en enquêtant les archives. Pour soutenir ces tâches difficiles, plusieurs solutions significatives issues des domaines de la recherche et du marché ont été proposées. Cependant, le manque de modèles génériques et précis pour la représentation du contenu vidéo fait de la construction d'un système intelligent et automatisé capable d'analyser et de décrire des vidéos une tâche ardue. De plus, le domaine d'application montre toujours un écart important entre le domaine de la recherche et les besoins réels, ainsi qu'un manque entre ces besoins réels et les outils d'analyse vidéo dans le marché. Par conséquence, jusqu'à présent dans les systèmes de surveillance conventionnels, la surveillance en direct et la recherche dans des archives reposent principalement sur des opérateurs humains. Cette thèse propose une nouvelle approche pour la description textuelle de contenus importants dans des scènes de vidéosurveillance, basée sur une nouvelle "ontologie VSSD" générique, sans contexte, centrée sur les interactions entre deux objets. L'ontologie proposée est générique, flexible et extensible, dédiée à la description de scènes de vidéosurveillance. Tout en analysant les différentes scènes vidéo, notre approche introduit de nombreux nouveaux concepts et méthodes concernant la médiation et l'action distante, la description synthétique, ainsi qu'une nouvelle façon de segmenter la vidéo et de classer les scènes. Nous introduisons une nouvelle méthode heuristique de distinction entre les objets déformables et non déformables dans les scènes. Nous proposons également des caractéristiques importantes pour une meilleure classification des interactions entre les objets vidéo, basée sur l'apprentissage, et une meilleure description.[...]Surveillance systems are important tools for law enforcement agencies for fighting crimes. Surveillance control rooms have two main duties: live monitoring the surveillance areas, and crime solving by investigating the archives. To support these difficult tasks, several significant solutions from the research and market fields have been proposed. However, the lack of generic and precise models for video content representation make the building of fully automated intelligent video analysis and description system a challenging task. Furthermore, the application domain still shows a big gap between the research field and the real practical needs, it also shows a lack between these real needs and the on-market video analytics tools. Consequently, in conventional surveillance systems, live monitoring and investigating the archives still rely mostly on human operators. This thesis proposes a novel approach for textual describing important contents in videos surveillance scenes, based on new generic context-free "VSSD ontology", with focus on two objects interactions. The proposed ontology presents a new generic flexible and extensible ontology dedicated for video surveillance scenes description. While analysing and understanding variety of video scenes, our approach introduces many new concepts and methods concerning mediation and action at a distant, abstraction in the description, and a new manner of categorizing the scenes. It introduces a new heuristic way to discriminate between deformable and non-deformable objects in the scenes. It also highlights and exports important features for better video objects interactions learning classifications and for better description. These features, if used as key parameters in video analytics tools, are much suitable for supporting surveillance systems operators through generating alerts, and intelligent search. Moreover, our system outputs can support police incidents reports, according to investigators needs, with many types of automatic textual description based on new well-structured rule-based schemas or templates. [...]
9
Semko, David A. "Optical flow analysis and Kalman Filter tracking in video surveillance algorithms." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FSemko.pdf.
Full textAbstract:
Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2007.Thesis Advisor(s): Monique P. Fargues. "June 2007." Includes bibliographical references (p. 69). Also available in print.
10
Wan, Yiwen. "Trajectories As a Unifying Cross Domain Feature for Surveillance Systems." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc699997/.
Full textAbstract:
Manual video analysis is apparently a tedious task. An efficient solution is of highly importance to automate the process and to assist operators. A major goal of video analysis is understanding and recognizing human activities captured by surveillance cameras, a very challenging problem; the activities can be either individual or interactional among multiple objects. It involves extraction of relevant spatial and temporal information from visual images. Most video analytics systems are constrained by specific environmental situations. Different domains may require different specific knowledge to express characteristics of interesting events. Spatial-temporal trajectories have been utilized to capture motion characteristics of activities. The focus of this dissertation is on how trajectories are utilized in assist in developing video analytic system in the context of surveillance. The research as reported in this dissertation begins real-time highway traffic monitoring and dynamic traffic pattern analysis and in the end generalize the knowledge to event and activity analysis in a broader context. The main contributions are: the use of the graph-theoretic dominant set approach to the classification of traffic trajectories; the ability to first partition the trajectory clusters using entry and exit point awareness to significantly improve the clustering effectiveness and to reduce the computational time and complexity in the on-line processing of new trajectories; A novel tracking method that uses the extended 3-D Hungarian algorithm with a Kalman filter to preserve the smoothness of motion; a novel camera calibration method to determine the second vanishing point with no operator assistance; and a logic reasoning framework together with a new set of context free LLEs which could be utilized across different domains. Additional efforts have been made for three comprehensive surveillance systems together with main contributions mentioned above.
11
Ellatif, FatahAllah Ibrahim Mahmoud Rashwan Hatem Abd. "Robust analysis and protection of dynamic scenes for privacy-aware video surveillance." Doctoral thesis, Universitat Rovira i Virgili, 2014. http://hdl.handle.net/10803/275966.
Full text
12
Yeung, Alex Tak Lok. "A competitive analysis of digital video surveillance products' manufacturers in Asia Pacific region." access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/dissert.pl?msc-meem-b1991300xa.pdf.
Full textAbstract:
Thesis (M.Sc.)--City University of Hong Kong, 2005.Title from title screen (viewed on Jan. 10, 2006) "A dissertation submitted in partial fulfillment of the requirements for the degree of Master of Science in Engineering Management." Includes bibliographical references.
13
Lubobya, Smart Charles. "Performance analysis and application development of hybrid WiMAX-WiFi IP video surveillance systems." Doctoral thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/25326.
Full textAbstract:
Traditional Closed Circuit Television (CCTV) analogue cameras installed in buildings and other areas of security interest necessitates the use of cable lines. However, analogue systems are limited by distance; and storing analogue data requires huge space or bandwidth. Wired systems are also prone to vandalism, they cannot be installed in a hostile terrain and in heritage sites, where cabling would distort original design. Currently, there is a paradigm shift towards wireless solutions (WiMAX, Wi-Fi, 3G, 4G) to complement and in some cases replace the wired system. A wireless solution of the Fourth-Generation Surveillance System (4GSS) has been proposed in this thesis. It is a hybrid WiMAX-WiFi video surveillance system. The performance analysis of the hybrid WiMAX-WiFi is compared with the conventional WiMAX surveillance models. The video surveillance models and the algorithm that exploit the advantages of both WiMAX and Wi-Fi for scenarios of fixed and mobile wireless cameras have been proposed, simulated and compared with the mathematical/analytical models. The hybrid WiMAX-WiFi video surveillance model has been extended to include a Wireless Mesh configuration on the Wi-Fi part, to improve the scalability and reliability. A performance analysis for hybrid WiMAX-WiFi system with an appropriate Mobility model has been considered for the case of mobile cameras. A security software application for mobile smartphones that sends surveillance images to either local or remote servers has been developed. The developed software has been tested, evaluated and deployed in low bandwidth Wi-Fi wireless network environments. WiMAX is a wireless metropolitan access network technology that provides broadband services to the connected customers. Major modules and units of WiMAX include the Customer Provided Equipment (CPE), the Access Service Network (ASN) which consist one or more Base Stations (BS) and the Connectivity Service Network (CSN). Various interfaces exist between each unit and module. WiMAX is based on the IEEE 802.16 family of standards. Wi-Fi, on the other hand, is a wireless access network operating in the local area network; and it is based on the IEEE 802.11 standards.
14
Kong, Lingchao. "Modeling of Video Quality for Automatic Video Analysis and Its Applications in Wireless Camera Networks." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563295836742645.
Full text
15
Tawiah, Thomas Andzi-Quainoo. "Video content analysis for automated detection and tracking of humans in CCTV surveillance applications." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/7344.
Full textAbstract:
The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented.
16
Kim, Kihwan. "Spatio-temporal data interpolation for dynamic scene analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47729.
Full textAbstract:
Analysis and visualization of dynamic scenes is often constrained by the amount of spatio-temporal information available from the environment. In most scenarios, we have to account for incomplete information and sparse motion data, requiring us to employ interpolation and approximation methods to fill for the missing information. Scattered data interpolation and approximation techniques have been widely used for solving the problem of completing surfaces and images with incomplete input data. We introduce approaches for such data interpolation and approximation from limited sensors, into the domain of analyzing and visualizing dynamic scenes. Data from dynamic scenes is subject to constraints due to the spatial layout of the scene and/or the configurations of video cameras in use. Such constraints include: (1) sparsely available cameras observing the scene, (2) limited field of view provided by the cameras in use, (3) incomplete motion at a specific moment, and (4) varying frame rates due to different exposures and resolutions.
In this thesis, we establish these forms of incompleteness in the scene, as spatio-temporal uncertainties, and propose solutions for resolving the uncertainties by applying scattered data approximation into a spatio-temporal domain.
The main contributions of this research are as follows: First, we provide an efficient framework to visualize large-scale dynamic scenes from distributed static videos. Second, we adopt Radial Basis Function (RBF) interpolation to the spatio-temporal domain to generate global motion tendency. The tendency, represented by a dense flow field, is used to optimally pan and tilt a video camera. Third, we propose a method to represent motion trajectories using stochastic vector fields. Gaussian Process Regression (GPR) is used to generate a dense vector field and the certainty of each vector in the field. The generated stochastic fields are used for recognizing motion patterns under varying frame-rate and incompleteness of the input videos. Fourth, we also show that the stochastic representation of vector field can also be used for modeling global tendency to detect the region of interests in dynamic scenes with camera motion. We evaluate and demonstrate our approaches in several applications for visualizing virtual cities, automating sports broadcasting, and recognizing traffic patterns in surveillance videos.
17
Laxhammar, Rikard. "Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications." Doctoral thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762.
Full textAbstract:
Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of anomalous trajectories in surveillance applications. The main results and contributions of the thesis are two-fold. Firstly, we propose and discuss a novel approach for anomaly detection, called conformal anomaly detection, which is based on conformal prediction (Vovk et al.). In particular, we propose two general algorithms for anomaly detection: the conformal anomaly detector (CAD) and the computationally more efficient inductive conformal anomaly detector (ICAD). A key property of conformal anomaly detection, in contrast to previous methods, is that it provides a well-founded approach for the tuning of the anomaly threshold that can be directly related to the expected or desired alarm rate. Secondly, we propose and analyse two parameter-light algorithms for unsupervised online learning and sequential detection of anomalous trajectories based on CAD and ICAD: the sequential Hausdorff nearest neighbours conformal anomaly detector (SHNN-CAD) and the sequential sub-trajectory local outlier inductive conformal anomaly detector (SSTLO-ICAD), which is more sensitive to local anomalous sub-trajectories. We implement the proposed algorithms and investigate their classification performance on a number of real and synthetic datasets from the video and maritime surveillance domains. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning on video trajectories. Moreover, we demonstrate that SSTLO-ICAD is able to accurately discriminate realistic anomalous vessel trajectories from normal background traffic.
18
Ben, hamida Amal. "Vers une nouvelle architecture de videosurveillance basée sur la scalabilité orientée vers l'application." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0144/document.
Full textAbstract:
Le travail présenté dans ce mémoire a pour objectif le développement d'une nouvelle architecture pour les systèmes de vidéosurveillance. Tout d'abord, une étude bibliographique nous a conduit à classer les systèmes existants selon le niveau de leurs applications qui dépend directement des fonctions analytiques exécutées. Nous avons également constaté que les systèmes habituels traitent toutes les données enregistrées alors que réellement une faible partie des scènes sont utiles pour l'analyse. Ainsi, nous avons étendu l'architecture ordinaire des systèmes de surveillance par une phase de pré-analyse qui extrait et simplifie les régions d'intérêt en conservant les caractéristiques importantes. Deux méthodes différentes pour la pré-analyse dans le contexte de la vidéosurveillance ont été proposées : une méthode de filtrage spatio-temporel et une technique de modélisation des objets en mouvement. Nous avons contribué, aussi, par l'introduction du concept de la scalabilité orientée vers l'application à travers une architecture multi-niveaux applicatifs pour les systèmes de surveillance. Les différents niveaux d'applications des systèmes de vidéosurveillance peuvent être atteints incrémentalement pour répondre aux besoins progressifs de l'utilisateur final. Un exemple de système de vidéosurveillance respectant cette architecture et utilisant nos méthodes de pré-analyse est proposéThe work presented in this thesis aims to develop a new architecture for video surveillance systems. Firstly, a literature review has led to classify the existing systems based on their applications level which dependents directly on the performed analytical functions. We, also, noticed that the usual systems treat all captured data while, actually, a small part of the scenes are useful for analysis. Hence, we extended the common architecture of surveillance systems with a pre-analysis phase that extracts and simplifies the regions of interest with keeping the important characteristics. Two different methods for preanalysis were proposed : a spatio-temporal filtering and a modeling technique for moving objects. We contributed, too, by introducing the concept of application-oriented scalability through a multi-level application architecture for surveillance systems. The different applications levels can be reached incrementally to meet the progressive needs of the enduser. An example of video surveillance system respecting this architecture and using the preanalysis methods was proposed
19
Conte, Donatello. "Detection, tracking, and behaviour analysis of moving people in intelligent video surveillance systems : a graph based approach." Lyon, INSA, 2006. http://theses.insa-lyon.fr/publication/2006ISAL0033/these.pdf.
Full textAbstract:
In this thesis video surveillance system is proposed. For each step of such systems it presents some innovations as regard as the state of the art in such systems. First of all, a new selectively and adaptively background substraction algorithm has been proposed to adapt the system at illumination and scene changes. Furthermore, some heuristics are proposed to solve object detection problems in real environment shadows, noise, etc. Result show that proposed techniques are robust in terms of quality of solution and, besides, they are efficient in terms of processing time. The main object of the thesis concerns the object tracking phase. In the thesis we propose a new algorithm based on a new representation of the objects : the graph pyramids. This presentation allows the resolutions of occlusions also in complex cases. They are preformed on standard datasets and standard indexes to provide objective results. The results show the approch is promisingDans cette thèse, nous proposons un système de vidéo surveillance qui présente des nouveaux algorithmes de détection d’objets et de suivi d’objets, afin de pallier les principaux problèmes qui se présentent dans le développement de tels systèmes. Il a été proposé un nouvel algorithme de soustraction du fond, sélectif et adaptatif, pour adapter le système à des changements de luminosité et de la structure de la scène. En outre, pour rendre applicable le système à des environnements réels, des heuristiques ont été proposées pour la résolution des différents problèmes : ombres, bruit, etc. Les résultats produits sur la phase de détection d’objets montrent que les techniques proposées sont robustes et utilisables en temps réels grâce à un temps de calcul peu élevé. L’objet principal de la thèse a concerné la phase de suivi d’objets. Dans cette thèse, nous proposons un nouvel algorithme basé sur une expérimentation des objets qui utilisent les pyramides de graphes. Des tests expérimentaux sur des bases de données standard et sur des index attestés pour l’évaluation des algorithmes de suivi d’objets en présence d’occlusions montrent que cette approche est très prometteuse
20
Conte, Donatello Jolion Jean-Michel Vento Mario. "Detection, tracking, and behaviour analysis of moving people in intelligent video surveillance systems a graph based approach /." Villeurbanne : Doc'INSA, 2006. http://docinsa.insa-lyon.fr/these/pont.php?id=conte.
Full text
21
Benfold, Ben. "The acquisition of coarse gaze estimates in visual surveillance." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:59186519-9fee-4005-9570-0e3cf0384447.
Full textAbstract:
This thesis describes the development of methods for automatically obtaining coarse gaze direction estimates for pedestrians in surveillance video. Gaze direction estimates are beneficial in the context of surveillance as an indicator of an individual's intentions and their interest in their surroundings and other people. The overall task is broken down into two problems. The first is that of tracking large numbers of pedestrians in low resolution video, which is required to identify the head regions within video frames. The second problem is to process the extracted head regions and estimate the direction in which the person is facing as a coarse estimate of their gaze direction. The first approach for head tracking combines image measurements from HOG head detections and KLT corner tracking using a Kalman filter, and can track the heads of many pedestrians simultaneously to output head regions with pixel-level accuracy. The second approach uses Markov-Chain Monte-Carlo Data Association (MCMCDA) within a temporal sliding window to provide similarly accurate head regions, but with improved speed and robustness. The improved system accurately tracks the heads of twenty pedestrians in 1920x1080 video in real-time and can track through total occlusions for short time periods. The approaches for gaze direction estimation all make use of randomised decision tree classifiers. The first develops classifiers for low resolution head images that are invariant to hair and skin colours using branch decisions based on abstract labels rather than direct image measurements. The second approach addresses higher resolution images using HOG descriptors and novel Colour Triplet Comparison (CTC) based branches. The final approach infers custom appearance models for individual scenes using weakly supervised learning over large datasets of approximately 500,000 images. A Conditional Random Field (CRF) models interactions between appearance information and walking directions to estimate gaze directions for head image sequences.
22
Ravulapalli, Sunil Babu. "Association of Sound to Motion in Video Using Perceptual Organization." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3769.
Full textAbstract:
Technological developments and innovations of the first forty years of the digital era have primarily addressed either the audio or the visual senses. Consequently, designers have primarily focused on the audio or the visual aspects of design. In the perspective of video surveillance, the data under consideration has always been visual. However, in light of the new behavioral and physiological studies which established a proof of cross modality in human perception i.e. humans do not process audio and visual stimulus separately, but percieve a scene based on all stimulus available, similar cues are being used to develop a surveillance system which uses both audio and visual data available. Human beings can easily associate a particular sound to an object in the surrounding. Drawing from such studies, we demonstrate a technique by which we can isolate concurrent audio and video events and associate them based on perceptual grouping principles. Associating sound to an object can form apart of larger surveillance system by producing a better description of objects.
We represent audio in the pitch-time domain and use image processing algorithms such as line detection to isolate significant events. These events and are then grouped based on gestalt principles of proximity and similarity which operates in audio. Once auditory events are isolated we can extract their periodicity. In video, we can extract objects by using simple background subtraction. We extract motion and shape periodicities of all the objects by tracking their position or the number of pixels in each frame. By comparing all the periodicities in audio and video using a simple index we can easily associate audio to video. We show results on five scenariosin outdoor settings with different kinds of human activity such as running, walking and other moving objects such as balls and cars.
23
Ye, Mang. "Open-world person re-identification." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/688.
Full textAbstract:
With the increasing demand of intelligent video surveillance systems, person re-identification (re-ID) plays an important role in intelligent video analysis, which aims at matching person images across non-overlapping camera views. It has gained increasing attention in computer vision community. With the advanced deep neural networks, existing methods have achieved promising performance on the widely-used re-ID benchmarks, even outperform the human-level rank-1 matching accuracy. However, most of the research efforts are conducted on the closed-world settings, with large-scale well annotated training data and all the person images are from the same visible modality. As a prerequisite in practical video surveillance application, there is still a large gap between the closed-world research-oriented setting and the practical open-world settings. In this thesis, we try to narrow the gap by studying three important issues in open-world person re-identification, including 1) unsupervised learning with large-scale unlabelled training data; 2) learning robust re-ID model with label corrupted training data and 3) cross-modality visible-thermal person re-identification with multi-modality data. For unsupervised learning with unlabelled training data, we mainly focus on video-based person re-identification, since the video data is usually easily obtained by tracking algorithms and the video sequence provides rich weakly labelled samples by assuming the image frames within the tracked sequence belonging to the same person identity. Following the cross-camera label estimation approach, we formulate the cross-camera label estimation as a one-to-one graph matching problem, and then propose a novel dynamic graph matching framework to estimate cross-camera labels. However, in a practical wild scenario, the unlabelled training data usually cannot satisfy the one-to-one matching constraint, which would result in a large proportion of false positives. To address this issue, we further propose a novel robust anchor embedding method for unsupervised video re-ID. In the proposed method, some anchor sequences are firstly selected to initialize the CNN feature representation. Then a robust anchor embedding method is proposed to measure the relationship between the unlabelled sequences and anchor sequences, which considers both the scalability and efficiency. After that, a top-{dollar}k{dollar} counts label prediction strategy is proposed to predict the labels of unlabelled sequences. With the newly estimated sequences, the CNN representation could be further updated. For robust re-ID model learning with label corrupted training data, we propose a two-stage learning method to handle the label noise. Rather than simply filtering the falsely annotated samples, we propose a joint learning method by simultaneously refining the falsely annotated labels and optimizing the neural networks. To address the limited training samples for each identity, we further propose a novel hard-aware instance re-weighting strategy to fine-tune the learned model, which assigns larger weights to hard samples with correct labels. For cross-modality visible-thermal person re-identification, it addresses an important issue in night-time surveillance applications by matching person images across different modalities. We propose a dual-path network to learn the cross-modality feature representations, which learns the multi-modality sharable feature representations by simultaneously considering the modality discrepancy and commonness. To guide the feature representation learning process, we propose a dual-constrained top-ranking loss, which contains both cross-modality and intra-modality top-ranking constraints to reduce the large cross-modality and intra-modality variations. Besides the open-world person re-identification, we have also studied the unsupervised embedding learning problem for general image classification and retrieval. Motivated by supervised embedding learning, we propose a data augmentation invariant and instance spread-out feature. To learn the feature embedding, we propose a instance feature-based softmax embedding, which optimizes the embedding directly on top of the real-time instance features. It achieves much faster learning speed and better accuracy than existing methods. In short, the major contributions of this thesis are summarized as follows. l A dynamic graph matching framework is proposed to estimate cross-camera labels for unsupervised video-based person re-identification. l A robust anchor embedding method with top-{dollar}k{dollar} counts label prediction is proposed to efficiently estimate the cross-camera labels for unsupervised video-based person re-identification under wild settings. l A two-stage PurifyNet is introduced to handle the label noise problem in person re-identification, which jointly refines the falsely annotated labels and mines hard samples with correct labels. l A dual-constrained top-ranking loss with a dual-path network is proposed for cross-modality visible-thermal person re-identification, which simultaneously addresses the cross-modality and intra-modality variations. l A data augmentation invariant and instance spread-out feature is proposed for unsupervised embedding learning, which directly optimizes the learned embedding on top of real-time instance features with softmax function
24
Luo, Zhiming. "Traffic analysis of low and ultra-low frame-rate videos." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/11854.
Full textAbstract:
Abstract: Nowadays, traffic analysis are relying on data collected from various traffic sensors. Among the various traffic surveillance techniques, video surveillance systems are often used for monitoring and characterizing traffic load. In this thesis, we focused on two aspects of traffic analysis without using motion features in low frame-rate videos: Traffic density flow analysis and Vehicle detection and classification. Traffic density flow analysis}: Knowing in real time when the traffic is fluid or when it jams is a key information to help authorities re-route vehicles and reduce congestion. Accurate and timely traffic flow information is strongly needed by individual travelers, the business sectors and government agencies. In this part, we investigated the possibility of monitoring highway traffic based on videos whose frame rate is too low to accurately estimate motion features. As we are focusing on analyzing traffic images and low frame-rate videos, traffic density is defined as the percentage of road being occupied by vehicles. In our previous work, we validated that traffic status is highly correlated to its texture features and that Convolutional Neural Networks (CNN) has the superiority of extracting discriminative texture features. We proposed several CNN models to segment traffic images into three different classes (road, car and background), classify traffic images into different categories (empty, fluid, heavy, jam) and predict traffic density without using any motion features. In order to generalize the model trained on a specific dataset to analyze new traffic scenes, we also proposed a novel transfer learning framework to do model adaptation. Vehicle detection and classification: The detection of vehicles pictured by traffic cameras is often the very first step of video surveillance systems, such as vehicle counting, tracking and retrieval. In this part, we explore different deep learning methods applied to vehicle detection and classification. Firstly, realizing the importance of large dataset for traffic analysis, we built and released the largest traffic dataset (MIO-TCD) in the world for vehicle localization and classification in collaboration with colleagues from Miovision inc. (Waterloo, On). With this dataset, we organized the Traffic Surveillance Workshop and Challenge in conjunction with CVPR 2017. Secondly, we evaluated several state-of-the-art deep learning methods for the classification and localization task on the MIO-TCD dataset. In light of the results, we may conclude that state-of-the-art deep learning methods exhibit a capacity to localize and recognize vehicle from a single video frame. While with a deep analysis of the results, we also identify scenarios for which state-of-the-art methods are still failing and propose concrete ideas for future work. Lastly, as saliency detection aims to highlight the most relevant objects in an image (e.g. vehicles in traffic scenes), we proposed a multi-resolution 4*5 grid CNN model for the salient object detection. The model enables near real-time high performance saliency detection. We also extend this model to do traffic analysis, experiment results show that our model can precisely segment foreground vehicles in traffic scenes.De nos jours, l’analyse de trafic routier est de plus en plus automatisée et s’appuie sur des données issues de senseurs en tout genre. Parmi les approches d’analyse de trafic routier figurent les méthodes à base de vidéo. Les méthodes à base de vidéo ont pour but d’identifier et de reconnaître les objets en mouvement (généralement des voitures et des piétons) et de comprendre leur dynamique. Un des défis parmi les plus difficile à résoudre est d’analyser des séquences vidéo dont le nombre d’images par seconde est très faible. Ce type de situation est pourtant fréquent considérant qu’il est très difficile (voir impossible) de transmettre et de stocker sur un serveur un très grand nombre d’images issues de plusieurs caméras. Dans ce cas, les méthodes issues de l’état de l’art échouent car un faible nombre d’images par seconde ne permet pas d’extraire les caractéristiques vidéos utilisées par ces méthodes tels le flux optique, la détection de mouvement et le suivi de véhicules. Au cours de cette thèse, nous nous sommes concentré sur l’analyse de trafic routier à partir de séquences vidéo contenant un très faible nombre d’images par seconde. Plus particulièrement, nous nous sommes concentrés sur les problème d’estimation de la densité du trafic routier et de la classification de véhicules. Pour ce faire, nous avons proposé différents modèles à base de réseaux de neurones profonds (plus particulièrement des réseaux à convolution) ainsi que de nouvelles bases de données permettant d’entraîner les dits modèles. Parmi ces bases de données figure « MIO-TCD », la plus grosse base de données annotées au monde faite pour l’analyse de trafic routier.
25
Leny, Marc. "Analyse et enrichissement de flux compressés : application à la vidéo surveillance." Thesis, Evry, Institut national des télécommunications, 2010. http://www.theses.fr/2010TELE0031/document.
Full textAbstract:
Le développement de réseaux de vidéosurveillance, civils ou militaires, pose des défis scientifiques et technologiques en termes d’analyse et de reconnaissance des contenus des flux compressés. Dans ce contexte, les contributions de cette thèse portent sur : - une méthode de segmentation automatique des objets mobiles (piétons, véhicules, animaux …) dans le domaine compressé, - la prise en compte des différents standards de compression les plus couramment utilisés en surveillance (MPEG-2, MPEG-4 Part 2 et MPEG-4 Part 10 / H.264 AVC), - une chaîne de traitement multi-flux optimisée depuis la segmentation des objets jusqu’à leur suivi et description. Le démonstrateur réalisé a permis d’évaluer les performances des approches méthodologiques développées dans le cadre d’un outil d’aide à l’investigation, identifiant les véhicules répondant à un signalement dans des bases de données de plusieurs dizaines d’heures. En outre, appliqué à des corpus représentatifs des différentes situations de vidéosurveillance (stations de métro, carrefours, surveillance de zones en milieu rural ou de frontières ...), le système a permis d’obtenir les résultats suivants : - analyse de 14 flux MPEG-2, 8 flux MPEG-4 Part 2 ou 3 flux AVC en temps réel sur un coeur à 2.66 GHZ (vidéo 720x576, 25 images par seconde), - taux de détection des véhicules de 100% sur la durée des séquences de surveillance de trafic, avec un taux de détection image par image proche des 95%, - segmentation de chaque objet sur 80 à 150% de sa surface (sous ou sur-segmentation liée au domaine compressé). Ces recherches ont fait l’objet du dépôt de 9 brevets liés à des nouveaux services et applications rendus opérationnels grâce aux approches mises en oeuvre. Citons entre autres des outils pour la protection inégale aux erreurs, la cryptographie visuelle, la vérification d’intégrité par tatouage ou l’enfouissement par stéganographieThe increasing deployment of civil and military videosurveillance networks brings both scientific and technological challenges regarding analysis and content recognition over compressed streams. In this context, the contributions of this thesis focus on: - an autonomous method to segment in the compressed domain mobile objects (pedestrians, vehicles, animals …), - the coverage of the various compression standards commonly used in surveillance (MPEG-2, MPEG-4 Part 2, MPEG-4 Part 10 / H.264 AVC), - an optimised multi-stream processing chain from the objects segmentation up to their tracking and description. The developed demonstrator made it possible to bench the performances of the methodological approaches chosen for a tool dedicated to help investigations. It identifies vehicles from a witness description in databases of tens of hours of video. Moreover, while dealing with corpus covering the different kind of content expected from surveillance (subway stations, crossroads, areas in countryside or border surveillance …), the system provided the following results: - simultaneous real time analysis of up to 14 MPEG-2 streams, 8 MPEG-4 Part 2 streams or 3 AVC streams on a single core (2.66 GHz; 720x576 video, 25 fps), - 100% vehicles detected over the length of traffic surveillance footages, with a image per image detection near 95%, - a segmentation spreading over 80 to 150% of the object area (under or over-segmentation linked with the compressed domain). These researches led to 9 patents linked with new services and applications that were made possible thanks to the suggested approaches. Among these lie tools for Unequal Error Protection, Visual Cryptography, Watermarking or Steganography
26
Azmat, Shoaib. "Multilayer background modeling under occlusions for spatio-temporal scene analysis." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/54005.
Full textAbstract:
This dissertation presents an efficient multilayer background modeling approach to distinguish among midground objects, the objects whose existence occurs over varying time scales between the extremes of short-term ephemeral appearances (foreground) and long-term stationary persistences (background). Traditional background modeling separates a given scene into foreground and background regions. However, the real world can be much more complex than this simple classification, and object appearance events often occur over varying time scales. There are situations in which objects appear on the scene at different points in time and become stationary; these objects can get occluded by one another, and can change positions or be removed from the scene. Inability to deal with such scenarios involving midground objects results in errors, such as ghost objects, miss-detection of occluding objects, aliasing caused by the objects that have left the scene but are not removed from the model, and new objects’ detection when existing objects are displaced. Modeling temporal layers of multiple objects allows us to overcome these errors, and enables the surveillance and summarization of scenes containing multiple midground objects.
27
Leoputra, Wilson Suryajaya. "Video foreground extraction for mobile camera platforms." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/1384.
Full textAbstract:
Foreground object detection is a fundamental task in computer vision with many applications in areas such as object tracking, event identification, and behavior analysis. Most conventional foreground object detection methods work only in a stable illumination environments using fixed cameras. In real-world applications, however, it is often the case that the algorithm needs to operate under the following challenging conditions: drastic lighting changes, object shape complexity, moving cameras, low frame capture rates, and low resolution images. This thesis presents four novel approaches for foreground object detection on real-world datasets using cameras deployed on moving vehicles.The first problem addresses passenger detection and tracking tasks for public transport buses investigating the problem of changing illumination conditions and low frame capture rates. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modelling method with a human shape model into a weighted Bayesian framework to detect passengers. To deal with the problem of tracking multiple targets, we employ the Reversible Jump Monte Carlo Markov Chain tracking algorithm. Using the SVM classifier, the appearance transformation models capture changes in the appearance of the foreground objects across two consecutives frames under low frame rate conditions. In the second problem, we present a system for pedestrian detection involving scenes captured by a mobile bus surveillance system. It integrates scene localization, foreground-background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data.In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarity, and the second stage further clusters these aligned frames according to consistency in illumination. This produces clusters of images that are differential in viewpoint and lighting. A kernel density estimation (KDE) technique for colour and gradient is then used to construct background models for each image cluster, which is further used to detect candidate foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be detected.In addition to the second problem, we present three direct pedestrian detection methods that extend the HOG (Histogram of Oriented Gradient) techniques (Dalal and Triggs, 2005) and provide a comparative evaluation of these approaches. The three approaches include: a) a new histogram feature, that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters (Leung and Malik, 2001) corresponding to the quantised orientation, which we refer to as the Histogram of Oriented Gradient Banks (HOGB) approach; b) the codebook based HOG feature with branch-and-bound (efficient subwindow search) algorithm (Lampert et al., 2008) and; c) the codebook based HOGB approach.In the third problem, a unified framework that combines 3D and 2D background modelling is proposed to detect scene changes using a camera mounted on a moving vehicle. The 3D scene is first reconstructed from a set of videos taken at different times. The 3D background modelling identifies inconsistent scene structures as foreground objects. For the 2D approach, foreground objects are detected using the spatio-temporal MRF algorithm. Finally, the 3D and 2D results are combined using morphological operations.The significance of these research is that it provides basic frameworks for automatic large-scale mobile surveillance applications and facilitates many higher-level applications such as object tracking and behaviour analysis.
28
Watt, James Robert. "Electronic workplace surveillance and employee privacy : a comparative analysis of privacy protection in Australia and the United States." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/26536/1/James_Watt_Thesis.pdf.
Full textAbstract:
More than a century ago in their definitive work “The Right to Privacy” Samuel D. Warren and Louis D. Brandeis highlighted the challenges posed to individual privacy by advancing technology. Today’s workplace is characterised by its reliance on computer technology, particularly the use of email and the Internet to perform critical business functions. Increasingly these and other workplace activities are the focus of monitoring by employers.
There is little formal regulation of electronic monitoring in Australian or United States workplaces. Without reasonable limits or controls, this has the potential to adversely affect employees’ privacy rights.
Australia has a history of legislating to protect privacy rights, whereas the United States has relied on a combination of constitutional guarantees, federal and state statutes, and the common law. This thesis examines a number of existing and proposed statutory and other workplace privacy laws in Australia and the United States.
The analysis demonstrates that existing measures fail to adequately regulate monitoring or provide employees with suitable remedies where unjustifiable intrusions occur. The thesis ultimately supports the view that enacting uniform legislation at the national level provides a more effective and comprehensive solution for both employers and employees.
Chapter One provides a general introduction and briefly discusses issues relevant to electronic monitoring in the workplace. Chapter Two contains an overview of privacy law as it relates to electronic monitoring in Australian and United States workplaces. In Chapter Three there is an examination of the complaint process and remedies available to a hypothetical employee (Mary) who is concerned about protecting her privacy rights at work. Chapter Four provides an analysis of the major themes emerging from the research, and also discusses the draft national uniform legislation. Chapter Five details the proposed legislation in the form of the Workplace Surveillance and Monitoring Act, and Chapter Six contains the conclusion.
29
Watt, James Robert. "Electronic workplace surveillance and employee privacy : a comparative analysis of privacy protection in Australia and the United States." Queensland University of Technology, 2009. http://eprints.qut.edu.au/26536/.
Full textAbstract:
More than a century ago in their definitive work “The Right to Privacy” Samuel D. Warren and Louis D. Brandeis highlighted the challenges posed to individual privacy by advancing technology. Today’s workplace is characterised by its reliance on computer technology, particularly the use of email and the Internet to perform critical business functions. Increasingly these and other workplace activities are the focus of monitoring by employers.
There is little formal regulation of electronic monitoring in Australian or United States workplaces. Without reasonable limits or controls, this has the potential to adversely affect employees’ privacy rights.
Australia has a history of legislating to protect privacy rights, whereas the United States has relied on a combination of constitutional guarantees, federal and state statutes, and the common law. This thesis examines a number of existing and proposed statutory and other workplace privacy laws in Australia and the United States.
The analysis demonstrates that existing measures fail to adequately regulate monitoring or provide employees with suitable remedies where unjustifiable intrusions occur. The thesis ultimately supports the view that enacting uniform legislation at the national level provides a more effective and comprehensive solution for both employers and employees.
Chapter One provides a general introduction and briefly discusses issues relevant to electronic monitoring in the workplace. Chapter Two contains an overview of privacy law as it relates to electronic monitoring in Australian and United States workplaces. In Chapter Three there is an examination of the complaint process and remedies available to a hypothetical employee (Mary) who is concerned about protecting her privacy rights at work. Chapter Four provides an analysis of the major themes emerging from the research, and also discusses the draft national uniform legislation. Chapter Five details the proposed legislation in the form of the Workplace Surveillance and Monitoring Act, and Chapter Six contains the conclusion.
30
Clayton, Sarah Elisabeth. "Tracking, analysis and measurement of pedestrian trajectories." Thesis, Edinburgh Napier University, 2016. http://researchrepository.napier.ac.uk/Output/452997.
Full textAbstract:
Pedestrian movement is unconstrained. For this reason it is not amenable to mathematical modelling in the same way as road trac. Individual pedestrians are notoriously difficult to monitor at a microscopic level. This has led to a lack of primary data that can be used to develop reliable models. Although video surveillance is cheap to install and operate, video data is extremely expensive to process for this purpose. An alternative approach is to use passive infrared detectors that are able to track individuals unobtrusively. This thesis describesthe use of a low cost infrared sensor for use in tracking pedestrians. The sensor itself, manufactured by a British company, is designed to count people crossing an arbitrary datum line. However, with the development of additional software, the functionality of these sensors can be extended beyond their original design specication. This allows the trajectories of individual pedestrians to be tracked. Although the field of view of each sensor is relatively small (44 m), five were deployed in a busy indoor corridor, covering most of its length. In this research, the technical challenges involved in using the sensors in this way are addressed. Statistics derived from the data collected are then compared to other studies at this scale.
31
Zhou, Y. "Analysing large-scale surveillance video." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3024330/.
Full textAbstract:
Analysing large-scale surveillance video has drawn signi cant attention because drone technology and high-resolution sensors are rapidly improving. The mobility of drones makes it possible to monitor a broad range of the environment, but it introduces a more di cult problem of identifying the objects of interest. This thesis aims to detect the moving objects (mostly vehicles) using the idea of background subtraction. Building a decent background is the key to success during the process. We consider two categories of surveillance videos in this thesis: when the scene is at and when pronounced parallax exists. After reviewing several global motion estimation approaches, we propose a novel cost function, the log-likelihood of the student t-distribution, to estimate the background motion between two frames. The proposed idea enables the estimation process to be e cient and robust with auto-generated parameters. Since the particle lter is useful in various subjects, it is investigated in this thesis. An improvement to particle lters, combining near-optimal proposal and Rao-Blackwellisation, is discussed to increase the e ciency when dealing with non-linear problems. Such improvement is used to solve visual simultaneous localisation and mapping (SLAM) problems and we call it RB2-PF. Its superiority is evident in both simulations of 2D SLAM and real datasets of visual odometry problems. Finally, RB2-PF based visual odometry is the key component to detect moving objects from surveillance videos with pronounced parallax. The idea is to consider multiple planes in the scene to improve the background motion estimation. Experiments have shown that false alarms signi cantly reduced. With the landmark information, a ground plane can be worked out. A near-constant velocity model can be applied after mapping the detections on the ground plane regardless of the position and orientation of the camera. All the detection results are nally processed by a multi-target tracker, the Gaussian mixture probabilistic hypothesis density (GM-PHD) lter, to generate tracks.
32
Lara, Arnaldo Camara. "Segmentação de movimento usando morfologia matemática." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-31072008-102401/.
Full textAbstract:
Esta dissertação apresenta um novo método de segmentação de movimento baseado na obtenção dos contornos e em filtros morfológicos. A nova técnica apresenta vantagens em relação ao número de falsos positivos e falsos negativos em situações específicas quando comparada às técnicas tradicionais.This work presents a novel motion segmentation technique based in contours and in morphological filters. It presents advantages in the number of false positives and false negatives in some situations when compared to the classic techniques.
33
Lin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/16703/1/Frank_Lin_Thesis.pdf.
Full textAbstract:
Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution.
As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions.
This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations.
A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.
34
Lin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Queensland University of Technology, 2008. http://eprints.qut.edu.au/16703/.
Full textAbstract:
Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution. As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions. This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations. A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.
35
Luo, Ning. "A Wireless Traffic Surveillance System Using Video Analytics." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc68005/.
Full textAbstract:
Video surveillance systems have been commonly used in transportation systems to support traffic monitoring, speed estimation, and incident detection. However, there are several challenges in developing and deploying such systems, including high development and maintenance costs, bandwidth bottleneck for long range link, and lack of advanced analytics. In this thesis, I leverage current wireless, video camera, and analytics technologies, and present a wireless traffic monitoring system. I first present an overview of the system. Then I describe the site investigation and several test links with different hardware/software configurations to demonstrate the effectiveness of the system. The system development process was documented to provide guidelines for future development. Furthermore, I propose a novel speed-estimation analytics algorithm that takes into consideration roads with slope angles. I prove the correctness of the algorithm theoretically, and validate the effectiveness of the algorithm experimentally. The experimental results on both synthetic and real dataset show that the algorithm is more accurate than the baseline algorithm 80% of the time. On average the accuracy improvement of speed estimation is over 3.7% even for very small slope angles.
36
Brulin, Mathieu. "Analyse sémantique d'un trafic routier dans un contexte de vidéo-surveillance." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14589/document.
Full textAbstract:
Les problématiques de sécurité, ainsi que le coût de moins en moins élevé des caméras numériques, amènent aujourd'hui à un développement rapide des systèmes de vidéosurveillance. Devant le nombre croissant de caméras et l'impossibilité de placer un opérateur humain devant chacune d'elles, il est nécessaire de mettre en oeuvre des outils d'analyse capables d'identifier des évènements spécifiques. Le travail présenté dans cette thèse s'inscrit dans le cadre d'une collaboration entre le Laboratoire Bordelais de Recherche en Informatique (LaBRI) et la société Adacis. L'objectif consiste à concevoir un système complet de vidéo-surveillance destiné à l'analyse automatique de scènes autoroutières et la détection d'incidents. Le système doit être autonome, le moins supervisé possible et doit fournir une détection en temps réel d'un évènement.Pour parvenir à cet objectif, l'approche utilisée se décompose en plusieurs étapes. Une étape d'analyse de bas-niveau, telle que l'estimation et la détection des régions en mouvement, une identification des caractéristiques d'un niveau sémantique plus élevé, telles que l'extraction des objets et la trajectoire des objets, et l'identification d'évènements ou de comportements particuliers, tel que le non respect des règles de sécurité. Les techniques employées s'appuient sur des modèles statistiques permettant de prendre en compte les incertitudes sur les mesures et observations (bruits d'acquisition, données manquantes, ...).Ainsi, la détection des régions en mouvement s'effectue au travers la modélisation de la couleur de l'arrière-plan. Le modèle statistique utilisé est un modèle de mélange de lois, permettant de caractériser la multi-modalité des valeurs prises par les pixels. L'estimation du flot optique, de la différence de gradient et la détection d'ombres et de reflets sont employées pour confirmer ou infirmer le résultat de la segmentation.L'étape de suivi repose sur un filtrage prédictif basé sur un modèle de mouvement à vitesse constante. Le cas particulier du filtrage de Kalman (filtrage tout gaussien) est employé, permettant de fournir une estimation a priori de la position des objets en se basant sur le modèle de mouvement prédéfini.L'étape d'analyse de comportement est constituée de deux approches : la première consiste à exploiter les informations obtenues dans les étapes précédentes de l'analyse. Autrement dit, il s'agit d'extraire et d'analyser chaque objet afin d'en étudier son comportement. La seconde étape consiste à détecter les évènements à travers une coupe du volume 2d+t de la vidéo. Les cartes spatio-temporelles obtenues sont utilisées pour estimer les statistiques du trafic, ainsi que pour détecter des évènements telles que l'arrêt des véhicules.Pour aider à la segmentation et au suivi des objets, un modèle de la structure de la scène et de ses caractéristiques est proposé. Ce modèle est construit à l'aide d'une étape d'apprentissage durant laquelle aucune intervention de l'utilisateur n'est requise. La construction du modèle s'effectue à travers l'analyse d'une séquence d'entraînement durant laquelle les contours de l'arrière-plan et les trajectoires typiques des véhicules sont estimés. Ces informations sont ensuite combinées pour fournit une estimation du point de fuite, les délimitations des voies de circulation et une approximation des lignes de profondeur dans l'image. En parallèle, un modèle statistique du sens de direction du trafic est proposé. La modélisation de données orientées nécessite l'utilisation de lois de distributions particulières, due à la nature périodique de la donnée. Un mélange de lois de type von-Mises est utilisée pour caractériser le sens de direction du traficAutomatic traffic monitoring plays an important role in traffic surveillance. Video cameras are relatively inexpensive surveillance tools, but necessitate robust, efficient and automated video analysis algorithms. The loss of information caused by the formation of images under perspective projection made the automatic task of detection and tracking vehicles a very challenging problem, but essential to extract a semantic interpretation of vehicles behaviors. The work proposed in this thesis comes from a collaboration between the LaBRI (Laboratoire Bordelais de Recherche en Informatique) and the company Adacis. The aim is to elaborate a complete video-surveillance system designed for automatic incident detection.To reach this objective, traffic scene analysis proceeds from low-level processing to high-level descriptions of the traffic, which can be in a wide variety of type: vehicles entering or exiting the scene, vehicles collisions, vehicles' speed that are too fast or too low, stopped vehicles or objects obstructing part of the road... A large number of road traffic monitoring systems are based on background subtraction techniques to segment the regions of interest of the image. Resulted regions are then tracked and trajectories are used to extract a semantic interpretation of the vehicles behaviors.The motion detection is based on a statistical model of background color. The model used is a mixture model of probabilistic laws, which allows to characterize multimodal distributions for each pixel. Estimation of optical flow, a gradient difference estimation and shadow and highlight detection are used to confirm or invalidate the segmentation results.The tracking process is based on a predictive filter using a motion model with constant velocity. A simple Kalman filter is employed, which allow to predict state of objets based on a \textit{a priori} information from the motion model.The behavior analysis step contains two approaches : the first one consists in exploiting information from low-level and mid-level analysis. Objects and their trajectories are analysed and used to extract abnormal behavior. The second approach consists in analysing a spatio-temporal slice in the 3D video volume. The extracted maps are used to estimate statistics about traffic and are used to detect abnormal behavior such as stopped vehicules or wrong way drivers.In order to help the segmentaion and the tracking processes, a structure model of the scene is proposed. This model is constructed using an unsupervised learning step. During this learning step, gradient information from the background image and typical trajectories of vehicles are estimated. The results are combined to estimate the vanishing point of the scene, the lanes boundaries and a rough depth estimation is performed. In parallel, a statistical model of the trafic flow direction is proposed. To deal with periodic data, a von-Mises mixture model is used to characterize the traffic flow direction
37
Cabon, Sandie. "Monitoring of premature newborns by video and audio analyses." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S055.
Full textAbstract:
L'objectif de ces travaux, conduits dans le cadre du projet européen Digi-NewB et d'une thèse CIFRE, était de proposer une nouvelle approche non-invasive de monitoring en unités de soins intensifs néonatales (NICU). Ce nouveau monitoring doit permettre d'évaluer de façon continue l'évolution neurocomportementale des nouveau-nés prématurés à partir de modalités non-invasives telles que la vidéo et l'audio. Après une étude bibliographique de plus de 150 documents, une première étude portant sur une estimation semi-automatique des stades de sommeil a été effectuée. L'approche proposée combinait pour la première fois des analyses vidéo et audio. Les limites identifiées lors de cette étude ont permis de proposer un nouveau système audio-vidéo et d'étudier son intégration en NICU. Des méthodes d'analyse vidéo, du son et de classification (Random Forest, KNN, Réseaux de Neurones…) ont été proposées. Elles permettent une caractérisation continue du comportement des nouveau-nés en termes de quantification des mouvements et d'analyse des pleurs. Les difficultés liées aux contraintes des conditions réelles de NICU ont été étudiées et des solutions pour écarter les périodes non analysables (e.g., parents ou personnel médical dans le champ de la caméra, alarmes provenant des appareils médicaux) ont été développées. Les résultats sont encourageants et montrent qu'il est aujourd'hui possible d'imaginer une nouvelle génération de monitoring basée sur des analyses non-invasives pour caractériser le développement neurocomportemental du nouveau-néThe objective of this work, conducted as part of the European project Digi-NewB and a CIFRE thesis, was to propose a new noninvasive approach to monitoring in neonatal intensive care units (NICUs). This new monitoring should make possible a continuous evaluation of the neuro-behavioural evolution of premature newborns using non-invasive modalities such as video and audio. After a bibliographical study of more than 150papers, a first study was carried out on a semiautomatic estimation of sleep stages. The proposed approach combined for the first time video and audio analyses. The limitations identified during this study led to the proposition of a new audio-video system. Its integration into NICU was studied and evaluated. Then, methods, based on video and audio processing techniques and classification (Random Forest, KNN, Multi-layer Perceptron ...), were proposed. They allow a continuous characterization of the newborn behaviour in terms of movement quantification and cry analysis. The difficulties related to the constraints of the real NICU conditions were studied and solutions to avoid irrelevant periods (e.g.,parents or medical staff in the camera field of view, alarms coming from medical devices) were developed. The results are encouraging and show that it is now possible to imagine a new generation of monitoring based on noninvasive analyses to characterize the neurobehavioural development of the newborn
38
Mihálik, Andrej. "Návrh elektronického zabezpečovacího systému jako část fyzického zabezpečení energetických objektů kritické infrastruktury." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2018. http://www.nusl.cz/ntk/nusl-378330.
Full textAbstract:
This master's thesis deals with the design of an electronic security system as part of the physical security for the energy company in the Czech Republic. The electronic security system is designed to meet all legal requirements, internal directives and has also passed ISO 27001 certification. The Implementation of the security system is demonstrated on the selected object of the company that belongs to the elements of the critical infrastructure.
39
Selmi, Mouna. "Reconnaissance d’activités humaines à partir de séquences vidéo." Thesis, Evry, Institut national des télécommunications, 2014. http://www.theses.fr/2014TELE0029/document.
Full textAbstract:
Cette thèse s’inscrit dans le contexte de la reconnaissance des activités à partir de séquences vidéo qui est une des préoccupations majeures dans le domaine de la vision par ordinateur. Les domaines d'application pour ces systèmes de vision sont nombreux notamment la vidéo surveillance, la recherche et l'indexation automatique de vidéos ou encore l'assistance aux personnes âgées. Cette tâche reste problématique étant donnée les grandes variations dans la manière de réaliser les activités, l'apparence de la personne et les variations des conditions d'acquisition des activités. L'objectif principal de ce travail de thèse est de proposer une méthode de reconnaissance efficace par rapport aux différents facteurs de variabilité. Les représentations basées sur les points d'intérêt ont montré leur efficacité dans les travaux d'art; elles ont été généralement couplées avec des méthodes de classification globales vue que ses primitives sont temporellement et spatialement désordonnées. Les travaux les plus récents atteignent des performances élevées en modélisant le contexte spatio-temporel des points d'intérêts par exemple certains travaux encodent le voisinage des points d'intérêt à plusieurs échelles. Nous proposons une méthode de reconnaissance des activités qui modélise explicitement l'aspect séquentiel des activités tout en exploitant la robustesse des points d'intérêts dans les conditions réelles. Nous commençons par l'extractivité des points d'intérêt dont a montré leur robustesse par rapport à l'identité de la personne par une étude tensorielle. Ces primitives sont ensuite représentées en tant qu'une séquence de sac de mots (BOW) locaux: la séquence vidéo est segmentée temporellement en utilisant la technique de fenêtre glissante et chacun des segments ainsi obtenu est représenté par BOW des points d'intérêt lui appartenant. Le premier niveau de notre système de classification séquentiel hybride consiste à appliquer les séparateurs à vaste marge (SVM) en tant que classifieur de bas niveau afin de convertir les BOWs locaux en des vecteurs de probabilités des classes d'activité. Les séquences de vecteurs de probabilité ainsi obtenues sot utilisées comme l'entrées de classifieur séquentiel conditionnel champ aléatoire caché (HCRF). Ce dernier permet de classifier d'une manière discriminante les séries temporelles tout en modélisant leurs structures internes via les états cachés. Nous avons évalué notre approche sur des bases publiques ayant des caractéristiques diverses. Les résultats atteints semblent être intéressant par rapport à celles des travaux de l'état de l'art. De plus, nous avons montré que l'utilisation de classifieur de bas niveau permet d'améliorer la performance de système de reconnaissance vue que le classifieur séquentiel HCRF traite directement des informations sémantiques des BOWs locaux, à savoir la probabilité de chacune des activités relativement au segment en question. De plus, les vecteurs de probabilités ont une dimension faible ce qui contribue à éviter le problème de sur apprentissage qui peut intervenir si la dimension de vecteur de caractéristique est plus importante que le nombre des données; ce qui le cas lorsqu'on utilise les BOWs qui sont généralement de dimension élevée. L'estimation les paramètres du HCRF dans un espace de dimension réduite permet aussi de réduire le temps d'entrainementHuman activity recognition (HAR) from video sequences is one of the major active research areas of computer vision. There are numerous application HAR systems, including video-surveillance, search and automatic indexing of videos, and the assistance of frail elderly. This task remains a challenge because of the huge variations in the way of performing activities, in the appearance of the person and in the variation of the acquisition conditions. The main objective of this thesis is to develop an efficient HAR method that is robust to different sources of variability. Approaches based on interest points have shown excellent state-of-the-art performance over the past years. They are generally related to global classification methods as these primitives are temporally and spatially disordered. More recent studies have achieved a high performance by modeling the spatial and temporal context of interest points by encoding, for instance, the neighborhood of the interest points over several scales. In this thesis, we propose a method of activity recognition based on a hybrid model Support Vector Machine - Hidden Conditional Random Field (SVM-HCRF) that models the sequential aspect of activities while exploiting the robustness of interest points in real conditions. We first extract the interest points and show their robustness with respect to the person's identity by a multilinear tensor analysis. These primitives are then represented as a sequence of local "Bags of Words" (BOW): The video is temporally fragmented using the sliding window technique and each of the segments thus obtained is represented by the BOW of interest points belonging to it. The first layer of our hybrid sequential classification system is a Support Vector Machine that converts each local BOW extracted from the video sequence into a vector of activity classes’ probabilities. The sequence of probability vectors thus obtained is used as input of the HCRF. The latter permits a discriminative classification of time series while modeling their internal structures via the hidden states. We have evaluated our approach on various human activity datasets. The results achieved are competitive with those of the current state of art. We have demonstrated, in fact, that the use of a low-level classifier (SVM) improves the performance of the recognition system since the sequential classifier HCRF directly exploits the semantic information from local BOWs, namely the probability of each activity relatively to the current local segment, rather than mere raw information from interest points. Furthermore, the probability vectors have a low-dimension which prevents significantly the risk of overfitting that can occur if the feature vector dimension is relatively high with respect to the training data size; this is precisely the case when using BOWs that generally have a very high dimension. The estimation of the HCRF parameters in a low dimension allows also to significantly reduce the duration of the HCRF training phase
40
Blake, Greyory. "Good Game." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5377.
Full textAbstract:
This thesis and its corresponding art installation, Lessons from Ziggy, attempts to deconstruct the variables prevalent within several complex systems, analyze their transformations, and propose a methodology for reasserting the soap box within the display pedestal. In this text, there are several key and specific examples of the transformation of various signifiers (i.e. media-bred fear’s transformation into a political tactic of surveillance, contemporary freneticism’s transformation into complacency, and community’s transformation into nationalism as a state weapon). In this essay, all of these concepts are contextualized within the exponential growth of new technologies. That is to say, all of these semiotic developments must be framed within the post-Internet sphere.
41
Hu, Yung-Hsiang, and 胡永祥. "Intelligent Video Analysis for Visual Surveillance over Mobile Networks." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/92630646771477524908.
Full textAbstract:
碩士輔仁大學
電子工程學系
95
This paper proposes an integrated intelligent surveillance system that uses Gaussian modeling, Morphology filters and Connected Component Labeling to detect moving objects. The system will automatically deliver Multimedia Messaging Service (MMS) alarms and WAP push notifications to user’s mobile phone, and then records and transcodes those frames of moving objects into video streaming file format after the real time key frame selection decided the most clearly frame of moving object. Users can monitor the recorded surveillance video streaming through 3G communication networks. Experimental results show that the system detects moving objects successfully, and the storage of object-based recording is more efficiently than full-time recording. It makes users monitor the surveillance video of moving objects in real time.
42
Chang, Chiang-Yu, and 張江伃. "Description and Analysis of Mouse Motion by Video Surveillance." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/49307161517425227999.
Full textAbstract:
碩士雲林科技大學
電機工程系碩士班
98
The purpose of this study is to detect the behavior of mouse mating and describe the mouse motion using video surveillance. It is in order to save the time of the observers and reduce the human misjudgment. The thesis in the beginning takes background subtraction to acquire the foreground objects, and then extracts the mouse object through image preprocessing. In the analysis of mouse mating, it starts with the object status from two separated ones into connected one. Then the object contour is detected from the edge information of the single foreground objects. The longest distance of all the possible two points in the object contour is measured to determine a major axis, and the line perpendicular to the major is the minor axis. Consequently, it will be further processed to obtain the conditions of the mouse mating. In mouse motion description, the distance of motion of object gravity center is used to determine the state of movement. A rectangular mask can be used to judge the state of rear. The variation of the mean square error (MSE) value between the objects in consecutive frames can be used to determine the state of sleep. The state of scratch can be determined through the use of three criteria. Experimental results show that the system can effectively detect mouse mating behavior and successfully describe the motions. In the analysis of the two mice mating, the accuracy can achieve 93.8%. In mouse motion description, the correct detection rates of the moving, rearing, and scratching are 99.7%, 96.8%, and 83.5%, respectively.
43
Lo, Chun-Chi, and 駱俊吉. "The Structural Description and Analysis of Big Surveillance Video." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/m8tkx5.
Full textAbstract:
碩士國立臺中科技大學
資訊工程系碩士班
104
With the increasing demand and higher standards for video surveillance systems, and to receive the messages from surveillance videos in real time, it is necessary to integrate the videos from surveillance cameras of different purposes and at different positions, so as to combine millions of surveillance videos. This project proposes a data structural description and analysis method for big surveillance videos, which can describe the big surveillance videos and provide users with a complete structure for quick searches. In regard to the data structural description of big surveillance videos, this study integrates the videos from surveillance servers in cloud server, and applies the videos according to the adaptive tracking algorithm of multiple objects proposed in this paper. After obtaining the region, color and texture feature of the objects being extracted, the sequential forward selection (SFS) is then used to select the optimal feature set of the object features. The objects are categorized into pedestrians, scooter and vehicles, to simplify the description of the surveillance video structure. Finally, this study proposes the structural description from the big surveillance videos based on object classification and object feature set. The structural description for the object of the big surveillance videos on the cloud server are translated into semantic content, and then analyzed, extracted and mined from the semantic content. Next, the object association dictionary of cloud videos is created based on relational hierarchical structure, which is called structural description of the big surveillance video. The experiments on object classification, object merger of multiple object tracking, pedestrians identification between multiple surveillance video, sequential forward selection (SFS), and establishment of objects-relational dictionary in order to verify the practicability of the proposed method. The experiment mainly uses pedestrian’s recognition for objects in the frame. First, we perform object classification; distinguish pedestrians from scooters and cars within the region of object in video, and efficiently tell pedestrians apart. Next, we experiment on merger of the similar object for reducing the number of object, it merges similar object to promote multiple feature of object and increases recognition rate in the future. Afterward we carry out pedestrian recognition from objects which are produced by different cameras; the experimental results show the proposed method can obtain good effect. Finally, we analyze pedestrians that are produced by different cameras, and get information about pedestrians that show up in camera; establish a pedestrians-relational dictionary for surveillance video based on relational hierarchical structure. The proposed method can efficiently establish pedestrian relevance in surveillance video. If it makes relational dictionary extended application in the future, it will provide administrator to rapidly select related video that pedestrians show up in different surveillance server and perform analysis.
44
Kan, Yao-Wen, and 甘耀文. "Smart City: Smart Cloud Transport Vehicles Video Surveillance Video Assessment Mechanism Analysis and Discourse." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/8um8u6.
Full textAbstract:
碩士華夏科技大學
資訊科技與管理研究所碩士在職專班
104
Transportation is what drives modern civilization forward. As the number of motorized vehicles increases, so does that of traffic accidents involving these vehicles in Taiwan every year, including traffic violations, criminal behavior patterns, traffic-related incidents and DUIs. These incidents do no decrease despite the efforts of the government to prevent them. According to the latest numbers of National Police Agency, Ministry of the Interior, 3,129 were killed in accidents involving motorized vehicles in 2013 alone, and this number was a staggering 2,103 in 2014 up to October. The financial expenses, manpower and medical supports were skyrocketing. When it comes to the major cause, it can be the poor images recorded by the surveillance cameras installed by local governments and administrations, inefficient equipment provided, lack of effective management, and low camera definition resulting in difficulties in providing evidence for local authorities either for justification or improvement. As a comparison, the number of traffic-related and criminal cases of unknown causes is also significant. As an effort to deal with the poor quality of surveillance equipment and inefficient management, this study was intended to investigate the possibility of establishing an “intelligent cloud-based transportation vehicle image tracking and evaluation mechanism” and image tracking analysis based on the concept of intelligent monitoring that meets the practical demands of a modern city. The idea was centered on dynamic identification of license plate. The image of a license plate was digitally converted and transmitted to an integrated cloud-based database management system for classification, management, analysis and application, as to improve the capability of digital monitoring of a metropolis. The existing license plate identification algorithm was studied and developed into an improved combination of active contour algorithm and improved differential algorithm. Based on this combination, a smart monitoring and dynamic license identification algorithm was realized specifically for an intelligent city. For verification, the 2nd place of the top ten locations of most accidents in New Taipei City, which is Zhongzheng Road in Zhonghe District, was chosen as the proving ground. For example, a 20-minute footage captured the image of 711 vehicles and 95.63% of license plates were scanned with 93.80% of correct identification. The experiment results indicated that the active license plate identification algorithm proposed achieved accurate multi-license locating and dividing techniques, license plate image noise removal and stable-unstable light source processing in complicated environment conditions and ensured accurate identification of license plates with the help of high-definition cameras. Also, the integration of cloud-based panning and management mechanism contributed to the achievement of tracking vehicles that escaped civil and criminal cases, clarification of responsibility in traffic accidents, identification of causes, and traffic flow statistics.
45
Chang, Wei-Shun, and 張惟舜. "A Video Surveillance Alarm System based on Human Behavior Analysis." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/46891481642915118393.
Full textAbstract:
碩士國立中山大學
資訊工程學系研究所
99
Human behavior analysis is an important challenge in many domains, such as surveillance systems, video content retrieval, human interactive systems, medical diagnosis, etc. With the increasing needs of public safety, intelligent surveillance system becomes an activating issue in computer vision and related research fields. In this thesis we present a method to analyze human behavior in a video sequence with depth information obtained from the depth camera. When interested actions are detected in the scene, the system will trigger alarm information. Contour line and Delaunay triangulation are used to establish human posture model. By traversing the triangulation meshes with the depth first search, we obtain the spanning tree with the depth information, and then construct human posture model with this spanning tree. Posture sequence from video sequence with corresponding posture models can be obtained, and then the posture sequences is clustered into key posture sequence. By querying the key posture sequence, the system can recognize human behavior in real-time and inform users immediately when interested actions detected. Experimental results show that the system is accurate and robust for human behavior recognition.
46
Zeng, Hui-Chi, and 曾惠淇. "Video Surveillance Analysis Based on Combining Foreground Extraction and Human Detection." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/64795062689139812787.
Full textAbstract:
碩士國立清華大學
資訊工程學系
95
In this thesis, we present an adaptive foreground object extraction algorithm for real-time video surveillance, in conjunction with a human detection technique applied in the extracted foreground regions by using AdaBoost learning algorithm and Histograms of Oriented Gradient (HOG) descriptors. Furthermore, a RANSAC-based temporal tracking algorithm is also applied to refine and trace the detected human windows in order to increase the detection accuracy and reduce the false alarm rate. The traditional background subtraction technique usually cannot work well for situations with lighting variations in the scene. The proposed algorithm employs a two-stage foreground/background classification procedure to perform background subtraction and remove the undesirable subtraction results due to shadow, automatic white balance, and sudden illumination change. After foreground extraction and human detection, the temporal information is utilized to increase the detection accuracy by performing the RANSAC-based temporal tracking to remove the false alarms and recover the missed detections. Experimental results on some real surveillance video are shown to demonstrate the good performance of the proposed adaptive foreground extraction algorithm under a variety of different environments with lighting variations and human detection system.
47
Kuo-Chung, Liu, and 劉國忠. "Organization of Taiwan Video Surveillance Industry analysis In eight Listed or Companies." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/h6s8mn.
Full textAbstract:
碩士國立臺北大學
社會學系
103
This paper focuses on the expansion of the growth of the technology industry of imitation and learning What is the difference? The organization in the company how to respond to changes in the industry during the heigly expansion? When industry technology faced with the Internet innovation, the organization learn how to imitate? How to convert new technology in existing niche markets? How to choose the conversion time point? The purpose of this thesis main study those companies in Taiwan cabinet video surveillance industry didn't seize market expansion with aggressive expansion of capital and human resources over the past decade. What factors have led companies to produce similar collective organizational behavior? What is effect of system and environment in Taiwan's capital market for securities companies? Second, in the continued expansion of the industry, why some companies are still able to stay afloat after a decade of advantage? Some companies are flat or decline? Those foreign competitors with actively expand the market and prices undermine policy impact on the domestic video surveillance of industrial ecology. What is the impact of new technologies on network marketer original analog technology products? Related theory of institutional theory, organizational ecology theory and technological innovation-oriented evolution theory, and for example ,Taiwan listed counters and eight video surveillance Enterprise. Discussion converted from analog video surveillance technology products to network technology industry transition and its influence. Study the relevant information to the securities market is mainly public information and various annual reports issued by these companies,and studying foreign competitors has the same information. Research found that companies will be affected by the stock market Cabinet policies and systems. Imitating each other leading companies meet investor expectations for corporate profit outstanding. Second, the development of imitation leads to homogeneity and groupthink.During industry transition disruptive innovation ,people neglect IP technology in low-end products market industry. Finally, companies will be delayed or do not react due to organizational inertia when companies faced organizational ecological changes. In addition to the organizational culture, the key lies in the establishment of the length of time the organization, and the organization niche dependence in the original ecosystem survival.
48
Liao, Yen-Kai, and 廖彥凱. "A Performance Analysis for Implementation of a Cloud-Based Video Surveillance System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/98je2d.
Full textAbstract:
碩士國立臺灣海洋大學
資訊工程學系
104
With the rapid development of technology, data volume is much bigger than before. Cloud technology and application in recent years has been a hot research topic not only for academic society but also for industries, e.g. Google, Amazon, to name a few, through ascension hardware and software technologies to provide better services to users. Traditionally, a high-end storage system with large storage capacity and fast data access is usually due to some sorts of customized design and thus cost much. Another important issues are such as scalability and fault-tolerance. Hadoop Distributed File System (HDFS) provides a cost-effective solution to the needs. In this thesis study, we investigate design of video surveillance systems, especially in the realm of video data storage and access. We built a video surveillance system prototype which incorporated HDFS as the storage subsystem for video file access. Based on the prototype system, we made intensive experiments to evaluate video file performance under different system considerations such as CPU virtualization, degree of data duplication, file access pattern, and file size.
49
Tung, Frederick. "Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance." Thesis, 2010. http://hdl.handle.net/10012/5241.
Full textAbstract:
Video surveillance systems are playing an increasing role in preventing and investigating crime, protecting public safety, and safeguarding national security. In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors like human fatigue and boredom.
The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features, this thesis improves a recently introduced approach that makes use of higher-level features of intentionality. Individuals in a scene are modelled as intentional agents instead of simply objects. Unusual behaviour detection then becomes a task of determining whether an agent's trajectory is explicable in terms of learned spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.
50
Chiu, I.-Hsuan, and 邱奕璿. "Using Scenario Analysis to Develop the Business Model for Video Surveillance as a Service." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/xcu3xq.
Full textAbstract:
碩士國立臺北科技大學
管理學院經營管理EMBA專班
101
Governments and enterprises all over the world rack their brains to stimulate consumption because globe economic growth slowed recently. However, more and more evidences show that traditional marketing and economic means cannot reach the target of economic growth. So, they expect to break through the dilemma by changing their mindset from product-oriented to service-oriented. Nevertheless, the future is uncertain and industries are impacted because of Internet and cloud computing. Thus the modification and adjustment of business model is important. Using scenario planning to explore IP based video surveillance trend, we got four reasonable scenarios. From economic point of view, we concluded high business model creation and high marketing demand scenario is potential one. In this particular scenario we think service-oriented will be core value proposition of Video Surveillance as a Service and pay-as-you-go could be a new revenue stream of intelligent video value-add service.