Relevant bibliographies by topics / Surveillance video analysis

Academic literature on the topic 'Surveillance video analysis'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Surveillance video analysis"

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Pal, Ratnabali, Arif Ahmed Sekh, Debi Prosad Dogra, Samarjit Kar, Partha Pratim Roy, and Dilip K. Prasad. "Topic-based Video Analysis." ACM Computing Surveys 54, no. 6 (July 2021): 1–34. http://dx.doi.org/10.1145/3459089.

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Manual processing of a large volume of video data captured through closed-circuit television is challenging due to various reasons. First, manual analysis is highly time-consuming. Moreover, as surveillance videos are recorded in dynamic conditions such as in the presence of camera motion, varying illumination, or occlusion, conventional supervised learning may not work always. Thus, computer vision-based automatic surveillance scene analysis is carried out in unsupervised ways. Topic modelling is one of the emerging fields used in unsupervised information processing. Topic modelling is used in text analysis, computer vision applications, and other areas involving spatio-temporal data. In this article, we discuss the scope, variations, and applications of topic modelling, particularly focusing on surveillance video analysis. We have provided a methodological survey on existing topic models, their features, underlying representations, characterization, and applications in visual surveillance’s perspective. Important research papers related to topic modelling in visual surveillance have been summarized and critically analyzed in this article.
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Kardas, Karani, and Nihan Kesim Cicekli. "SVAS: Surveillance Video Analysis System." Expert Systems with Applications 89 (December 2017): 343–61. http://dx.doi.org/10.1016/j.eswa.2017.07.051.

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Nguyen, Cuong, Wu-chi Feng, and Feng Liu. "Hotspot: Making computer vision more effective for human video surveillance." Information Visualization 15, no. 4 (July 25, 2016): 273–85. http://dx.doi.org/10.1177/1473871616630015.

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Studies have shown that the human capability of monitoring multiple surveillance videos is limited. Computer vision techniques have been developed to detect abnormal events to support human video surveillance; however, their results are often unreliable, thus distracting surveillance operators and making them miss important events. This article presents Hotspot as a surveillance video visualization system that can effectively leverage noisy computer vision techniques to support human video surveillance. Hotspot consists of two views: a designated focus view to summarize videos with detected events and a video-bank view surrounding the focus view to display source surveillance videos. The focus view allows an operator to quickly dismiss false alarms and focus on true alarms. The video-bank view allows for extended human video analysis after an important event is detected. Hotspot further provides visual links to assist quick attention switch from the focus view to the video-bank view. Our experiments show that Hotspot can effectively integrate noisy, automatic computer vision detection results and better support human video surveillance tasks than the baseline video surveillance with no or only basic computer vision support.
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Talvitie-Lamberg, Karoliina. "Video Streaming and Internalized Surveillance." Surveillance & Society 16, no. 2 (July 14, 2018): 238–57. http://dx.doi.org/10.24908/ss.v16i2.6407.

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This paper aims to develop knowledge about the complicated ways in which the modern individual uses surveillance (techniques) and the ways surveillance uses the individual. My observational analysis of a videostreaming community reveals the central role that surveillance plays in participating and becoming visible in an online environment. The results show that through disciplinary and lateral surveillance, participants produced context-defined I-narrations and formed themselves following the normative judgment of the environment. The same mechanism may be observed in other videostreaming social media environments and the modern social media-saturated society in general. This is an inconspicuous way to produce surveillant individualism. Contrary to the notion of exploitative participation, this study reveals the productive power of surveillance. My research suggests that disciplinary power is integrated into the everyday in online DIY environments and it creates the space and framework for communication in these environments. Surveillance practices offer empowering means for forming identities.
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Pan, Tung-Ming, Kuo-Chin Fan, and Yuan-Kai Wang. "Object-Based Approach for Adaptive Source Coding of Surveillance Video." Applied Sciences 9, no. 10 (May 16, 2019): 2003. http://dx.doi.org/10.3390/app9102003.

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Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.
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De Meneses, Y. L., P. Roduit, F. Luisier, and J. Jacot. "Trajectory Analysis for Sport and Video Surveillance." ELCVIA Electronic Letters on Computer Vision and Image Analysis 5, no. 3 (November 1, 2005): 148. http://dx.doi.org/10.5565/rev/elcvia.113.

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Taha, Ahmed, Hala H. Zayed, M. E. Khalifa, and El-Sayed M. El-Horbaty. "Exploring Behavior Analysis in Video Surveillance Applications." International Journal of Computer Applications 93, no. 14 (May 16, 2014): 22–32. http://dx.doi.org/10.5120/16283-6045.

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Zhu, Tao, and Wei Jun Hong. "Effect Evaluation of Video Surveillance System on the Basis of Principal Component Analysis." Applied Mechanics and Materials 713-715 (January 2015): 479–81. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.479.

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The effect evaluation of video surveillance system is important for the effect of expected protection on the system. A comprehensive effect evaluation index system of video surveillance system is established. The Principal Component Analysis (PCA) method is applied on the established index system to obtain a new evaluation index system. It is proved in instances that the effect evaluation method of video surveillance system with the application of the index system is capable of evaluating the video surveillance system effectively and quantitatively. The protective effect of the video surveillance system is evaluated objectively on the basis of the new index system with the PCA.
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Xie, Feng, and Zheng Xu. "Semantic Based Annotation for Surveillance Big Data Using Domain Knowledge." International Journal of Cognitive Informatics and Natural Intelligence 9, no. 1 (January 2015): 16–29. http://dx.doi.org/10.4018/ijcini.2015010102.

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Video surveillance technology is playing a more and more important role in traffic detection. Vehicle's static properties are crucial information in examining criminal and traffic violations. Image and video resources play an important role in traffic events analysis. With the rapid growth of the video surveillance devices, large number of image and video resources is increasing being created. It is crucial to explore, share, reuse, and link these multimedia resources for better organizing traffic events. With the development of Video Surveillance technology, it has been wildly used in the traffic monitoring. Therefore, there is a trend to use Video Surveillance to do intelligent analysis on vehicles. Now, using software and tools to analyze vehicles in videos has already been used in smart cards and electronic eye, which helps polices to extract useful information like plate, speed, etc. And the key technology is to obtain various properties of the vehicle. This paper provides an overview of the algorithms and technologies used in extracting static properties of vehicle in the video.
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WARNICK, BRYAN. "Surveillance Cameras in Schools: An Ethical Analysis." Harvard Educational Review 77, no. 3 (September 1, 2007): 317–43. http://dx.doi.org/10.17763/haer.77.3.r2k76507rrjw8238.

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In this essay, Bryan R. Warnick responds to the increasing use of surveillance cameras in public schools by examining the ethical questions raised by their use. He explores the extent of a student's right to privacy in schools, stipulates how video surveillance is similar to and different from commonly accepted in-person surveillance practices, and discusses the possible impact of surveillance technology on educational environments. In response to the ethical concerns he raises, Warnick offers five suggestions for how schools can use video surveillance technology in more ethically sensitive ways.
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Dissertations / Theses on the topic "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.

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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.
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Li, Hao. "Advanced video analysis for surveillance applications." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555815.

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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.
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Yoon, Kyongil. "Key-frame appearance analysis for video surveillance." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2818.

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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.
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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.

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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.
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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.

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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 groups
people 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.
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Sutor, S. R. (Stephan R. ). "Large-scale high-performance video surveillance." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205618.

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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 connected
Tiivistelmä 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
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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.

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Youssef, Wael Farid. "Instanciation d'un schéma de description textuel de scènes de vidéo surveillance." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30249.

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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. [...]
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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.

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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.
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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/.

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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.
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Books on the topic "Surveillance video analysis"

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Automated surveillance: A guide to intelligent video analysis. [Chatswood, N.S.W.]: [IOmniscient], 2009.

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Roy-Chowdhury, Amit K. Camera networks: The acquisition and analysis of videos over wide areas. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2012.

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Multimedia information extraction: Advances in video, audio, and imagery analysis for search, data mining, surveillance, and authoring. Hoboken, N.J: Wiley, 2012.

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(Korea), Kungnip Pangjae Yŏn'guso. Chinŭnghyŏng yŏngsang chŏngbo insik kisul ŭl iyong han chaenan kwalli kodohwa kibŏp kaebal =: Advancement of disaster management techniques for intelligent video contents analysis. Sŏul T'ŭkpyŏlsi: Kungnip Pangjae Kyoyugwŏn Yŏn'guwŏn, Pangjae Yŏn'guso, 2010.

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Coelho, Alessandra Martins. Multimedia Networking and Coding: State-of-the Art Motion Estimation in the Context of 3D TV. Cyprus: INTECH, 2013.

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Stout, Dorothy. Surveillance Video Enhancement, Analysis, and Interpretation: Basics for Forensic Investigation and Crime Prevention. CRC, 2009.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. Wiley & Sons, Limited, John, 2012.

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Book chapters on the topic "Surveillance video analysis"

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Rodriguez, Mikel, Josef Sivic, and Ivan Laptev. "Analysis of Crowded Scenes in Video." In Intelligent Video Surveillance Systems, 251–72. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118577851.ch15.

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Leny, Marc. "Compressed Domain Analysis for Fast Activity Detection." In Intelligent Video Surveillance Systems, 87–102. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118577851.ch6.

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Kolekar, Maheshkumar H. "Basics of Video Compression and Motion Analysis." In Intelligent Video Surveillance Systems, 37–58. First edition. | Boca Raton, Florida : CRC Press/Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315153865-2.

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Varona, Javier, Jordi Gonzàlez, F. Xavier Roca, and J. J. Villanueva. "Appearance Tracking for Video Surveillance." In Pattern Recognition and Image Analysis, 1041–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_120.

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Emonet, Rémi, and Jean-Marc Odobez. "Unsupervised Methods for Activity Analysis and Detection of Abnormal Events." In Intelligent Video Surveillance Systems, 219–34. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118577851.ch13.

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Cavallaro, Andrea, and Francesco Ziliani. "Image Analysis for Advanced Video Surveillance." In Multimedia Video-Based Surveillance Systems, 57–67. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4327-5_6.

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Murphey, Yi L., Henry Lu, Robert Karlsen, Grant Gerhart, and Thomas Meitzler. "Dyta: An Intelligent System for Dynamic Target Analysis." In Multimedia Video-Based Surveillance Systems, 118–29. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4327-5_11.

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Greiffenhagen, Michael, and Visvanathan Ramesh. "Performance Analysis of Multi- Sensor Based Real-Time People Detection and Tracking System." In Multimedia Video-Based Surveillance Systems, 224–37. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4327-5_19.

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Mayer, Brandon A., and Joseph L. Mundy. "Change Point Geometry for Change Detection in Surveillance Video." In Image Analysis, 377–87. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19665-7_31.

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Davis, Michael, Stefan Popov, and Cristina Surlea. "Real-Time Face Recognition from Surveillance Video." In Intelligent Video Event Analysis and Understanding, 155–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17554-1_8.

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Conference papers on the topic "Surveillance video analysis"

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Cantoni, Virginio, Roberto Marmo, and Marco Zemblini. "Video Surveillance and SOS Request." In 14th International Conference on Image Analysis and Processing (ICIAP 2007). IEEE, 2007. http://dx.doi.org/10.1109/iciap.2007.4362837.

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Hao Zong-Bo, Sang Nan, Li Chang-Lin, and Xu Xin. "Video surveillance based on energy feature." In 2010 International Conference on Apperceiving Computing and Intelligence Analysis (ICACIA). IEEE, 2010. http://dx.doi.org/10.1109/icacia.2010.5709900.

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Cheng, Michael, Binh Pham, and Dian Tjondronegoro. "Tracking and video surveillance activity analysis." In the 4th international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1174429.1174491.

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Zhao, Lei, Xiang Zhang, Xinfeng Zhang, Shiqi Wang, Shanshe Wang, Siwei Ma, and Wen Gao. "Intelligent analysis oriented surveillance video coding." In 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017. http://dx.doi.org/10.1109/icme.2017.8019429.

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Arraiza Irujo, Juan, Montse Cuadros, Naiara Aginako, Matteo Raffaelli, Olga Kaehm, Naser Damer, and Joao P. Neto. "Multimedia Analysis of Video Sources." In Special Session on Multimodal Security and Surveillance Analytics. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005126903460352.

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Zhihai He and Dapeng Wu. "Performance analysis of wireless video sensors in video surveillance." In GLOBECOM '05. IEEE Global Telecommunications Conference, 2005. IEEE, 2005. http://dx.doi.org/10.1109/glocom.2005.1577376.

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Brulin, M., H. Nicolas, and C. Maillet. "Video Surveillance Traffic Analysis Using Scene Geometry." In 2010 Fourth Pacific-Rim Symposium on Image and Video Technology (PSIVT). IEEE, 2010. http://dx.doi.org/10.1109/psivt.2010.82.

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Jinghua Wang and Guoyan Zhang. "Video data mining based on K-Means algorithm for surveillance video." In 2011 International Conference on Image Analysis and Signal Processing (IASP). IEEE, 2011. http://dx.doi.org/10.1109/iasp.2011.6109120.

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San Miguel, Juan Carlos, Jesús Bescós, José M. Martínez, and Álvaro García. "DiVA: A Distributed Video Analysis Framework Applied to Video-Surveillance Systems." In 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services. IEEE, 2008. http://dx.doi.org/10.1109/wiamis.2008.29.

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Suvonvorn, Nikom. "A video analysis framework for surveillance system." In 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP). IEEE, 2008. http://dx.doi.org/10.1109/mmsp.2008.4665195.

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