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Статті в журналах з теми "Surveillance video analysis"
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.
Повний текст джерелаKardas, 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.
Повний текст джерелаNguyen, 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.
Повний текст джерелаTalvitie-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.
Повний текст джерелаPan, 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.
Повний текст джерелаDe 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.
Повний текст джерелаTaha, 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.
Повний текст джерелаZhu, 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.
Повний текст джерелаXie, 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.
Повний текст джерелаWARNICK, 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.
Повний текст джерелаДисертації з теми "Surveillance video analysis"
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 джерелаКниги з теми "Surveillance video analysis"
Automated surveillance: A guide to intelligent video analysis. [Chatswood, N.S.W.]: [IOmniscient], 2009.
Знайти повний текст джерелаRoy-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.
Знайти повний текст джерелаMultimedia information extraction: Advances in video, audio, and imagery analysis for search, data mining, surveillance, and authoring. Hoboken, N.J: Wiley, 2012.
Знайти повний текст джерела(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.
Знайти повний текст джерелаCoelho, Alessandra Martins. Multimedia Networking and Coding: State-of-the Art Motion Estimation in the Context of 3D TV. Cyprus: INTECH, 2013.
Знайти повний текст джерелаStout, Dorothy. Surveillance Video Enhancement, Analysis, and Interpretation: Basics for Forensic Investigation and Crime Prevention. CRC, 2009.
Знайти повний текст джерелаMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.
Знайти повний текст джерелаMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.
Знайти повний текст джерелаMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.
Знайти повний текст джерелаMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. Wiley & Sons, Limited, John, 2012.
Знайти повний текст джерелаЧастини книг з теми "Surveillance video analysis"
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.
Повний текст джерелаLeny, 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.
Повний текст джерелаKolekar, 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.
Повний текст джерелаVarona, 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.
Повний текст джерелаEmonet, 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.
Повний текст джерелаCavallaro, 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.
Повний текст джерелаMurphey, 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.
Повний текст джерелаGreiffenhagen, 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.
Повний текст джерелаMayer, 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.
Повний текст джерелаDavis, 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.
Повний текст джерелаТези доповідей конференцій з теми "Surveillance video analysis"
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.
Повний текст джерелаHao 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.
Повний текст джерелаCheng, 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.
Повний текст джерелаZhao, 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.
Повний текст джерелаArraiza 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.
Повний текст джерелаZhihai 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.
Повний текст джерелаBrulin, 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.
Повний текст джерелаJinghua 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.
Повний текст джерелаSan 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.
Повний текст джерелаSuvonvorn, 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.
Повний текст джерела