Дисертації з теми "Surveillance video analysis"
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Bales, Michael Ryan. "Illumination compensation in video surveillance analysis." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39535.
Повний текст джерелаLi, Hao. "Advanced video analysis for surveillance applications." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555815.
Повний текст джерелаYoon, Kyongil. "Key-frame appearance analysis for video surveillance." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2818.
Повний текст джерелаThesis 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.
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.
Повний текст джерелаCommittee 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.
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.
Повний текст джерелаpeople 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.
Sutor, S. R. (Stephan R. ). "Large-scale high-performance video surveillance." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205618.
Повний текст джерелаTiivistelmä 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
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.
Повний текст джерелаYoussef, Wael Farid. "Instanciation d'un schéma de description textuel de scènes de vidéo surveillance." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30249.
Повний текст джерелаSurveillance 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. [...]
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.
Повний текст джерелаThesis Advisor(s): Monique P. Fargues. "June 2007." Includes bibliographical references (p. 69). Also available in print.
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/.
Повний текст джерелаEllatif, 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.
Повний текст джерелаYeung, 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.
Повний текст джерелаTitle 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.
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.
Повний текст джерелаKong, 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.
Повний текст джерелаTawiah, 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.
Повний текст джерелаKim, Kihwan. "Spatio-temporal data interpolation for dynamic scene analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47729.
Повний текст джерелаLaxhammar, 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.
Повний текст джерелаBen, 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.
Повний текст джерелаThe 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
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.
Повний текст джерелаDans 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
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.
Повний текст джерелаBenfold, 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.
Повний текст джерелаRavulapalli, Sunil Babu. "Association of Sound to Motion in Video Using Perceptual Organization." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3769.
Повний текст джерелаYe, Mang. "Open-world person re-identification." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/688.
Повний текст джерелаLuo, Zhiming. "Traffic analysis of low and ultra-low frame-rate videos." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/11854.
Повний текст джерелаDe 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.
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.
Повний текст джерелаThe 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
Azmat, Shoaib. "Multilayer background modeling under occlusions for spatio-temporal scene analysis." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/54005.
Повний текст джерелаLeoputra, Wilson Suryajaya. "Video foreground extraction for mobile camera platforms." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/1384.
Повний текст джерелаWatt, 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.
Повний текст джерелаWatt, 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/.
Повний текст джерелаClayton, Sarah Elisabeth. "Tracking, analysis and measurement of pedestrian trajectories." Thesis, Edinburgh Napier University, 2016. http://researchrepository.napier.ac.uk/Output/452997.
Повний текст джерелаZhou, Y. "Analysing large-scale surveillance video." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3024330/.
Повний текст джерелаLara, 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/.
Повний текст джерелаThis 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.
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.
Повний текст джерелаLin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Queensland University of Technology, 2008. http://eprints.qut.edu.au/16703/.
Повний текст джерелаLuo, Ning. "A Wireless Traffic Surveillance System Using Video Analytics." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc68005/.
Повний текст джерелаBrulin, Mathieu. "Analyse sémantique d'un trafic routier dans un contexte de vidéo-surveillance." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14589/document.
Повний текст джерелаAutomatic 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
Cabon, Sandie. "Monitoring of premature newborns by video and audio analyses." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S055.
Повний текст джерелаThe 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
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.
Повний текст джерелаSelmi, Mouna. "Reconnaissance d’activités humaines à partir de séquences vidéo." Thesis, Evry, Institut national des télécommunications, 2014. http://www.theses.fr/2014TELE0029/document.
Повний текст джерелаHuman 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
Blake, Greyory. "Good Game." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5377.
Повний текст джерелаHu, Yung-Hsiang, and 胡永祥. "Intelligent Video Analysis for Visual Surveillance over Mobile Networks." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/92630646771477524908.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
Tung, Frederick. "Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance." Thesis, 2010. http://hdl.handle.net/10012/5241.
Повний текст джерелаChiu, 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.
Повний текст джерела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.