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Aktualne trendy w tworzeniu systemów inteligentnego monitoringu wizyjnego

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Warianty tytułu
Języki publikacji
PL
Abstrakty
EN
This article provides an overview and critical analysis of computer vision algorithms used in the construction of the modules responsible for recognition and identification of objects which are elements of an intelligent monitoring system. We investigate state-of-the-art methods with an enough high potential to be implemented in a practical realization of such a system. The article describes three main elements of modern surveillance system, namely an adaptive background model, object extraction and tracking. Finally, we describe several recent benchmark datasets that can be used to test real systems.
Rocznik
Tom
Strony
19--39
Opis fizyczny
Bibliogr. 39 poz., rys.
Twórcy
autor
  • Zachodniopomorski Uniwersytet Technologiczny w Szczecinie, Wydział Informatyki
Bibliografia
  • [1] Robertson N., Reid I. A general method for human activity recognition in video. Computer Vision and Image Understanding, vol.104, 2006, s. 232–248
  • [2] Pantrigo J.J., Hernández, J., Sánchez A. Multiple and variable target visual tracking for video-surveillance applications. Pattern Recognition Letters, vol. 31, 2010, s. 1577–1590
  • [3] Gurwicz Y., Yehezkel R., Lachover B. Multiclass object classification for real-time video surveillance systems. Pattern Recognition Letters, vol. 32, 2011, s. 805–815
  • [4] Calderara S., Heinemann U., Prati A., Cucchiara R., Tishby N., Detecting anomalies in people’s trajectories using spectral graph analysis. Computer Vision and Image Understanding, vol. 115, 2011, s. 1099–1111
  • [5] Zhou H., Hu H., Human motion tracking for rehabilitation—A survey. Biomedical Signal Processing and Control 3, 2008, s. 1–18
  • [6] Douglas A., Mubarak S. Monitoring human behavior from video taken in an office environment. Image and Vision Computing, vo. 19, 2001, s. 833-846 Aktualne trendy w tworzeniu systemów inteligentnego monitoringu wizyjnego 31
  • [7] Ivanov Y., Bobick A., Liu J. Fast lighting independent background subtraction. In IEEE Workshop on Visual Surveillance, 1998, s. 49-55
  • [8] Lim S., Mittal A., Davis L. S., Paragios N. Fast illumination-invariant background subtraction using two views: Error analysis, sensor placement and applications. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, 2005, s. 1071-1078
  • [9] Frejlichowski D. Automatic Localisation of Moving Vehicles in Image Sequences Using Morphological Opertion. 1st IEEE International Conference on Information Technology, 2008, s. 439 - 442
  • [10] Stauffer C., Grimson W. E. L. Adaptive background mixture models for real-time tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, 2: 252
  • [11] Zivkovic Z. Improved adaptive gaussian mixture model for background subtraction. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., vol. 2, 2004, s. 28-31
  • [12] Kaewtrakulpong P., Bowden R. An improved adaptive background mixture model for realtime tracking with shadow detection. In Proceeding of the 2nd European Workshop on Advanced Video Based Surveillance Systems, Computer Vision and Distributed Processing, Kluver Academic Publishers, 2001, s. 1-5
  • [13] Javed O., Shafique K., Shah M. A hierarchical approach to robust background subtraction using color and gradient information. In Workshop on Motion and Video Computing, 2002, s. 22-27
  • [14] Zhang H., Xu D. Fusing color and gradient features for background model. In 8th International Conference on Signal Processing, vol.2, no., 16-20, 2006
  • [15] Wang H., Suter D. A re-evaluation of mixture of gaussian background modeling [video signal processing applications]. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), 2005, vol. 2, s. 1017-1020
  • [16] Piccardi M. Background subtraction techniques: a review. In IEEE International Conference on Systems, Man and Cybernetics, 2004, vol. 4, s. 3099-3104
  • [17] Gao X., Boult T.E., Ramesh V., Coetzee F. Error analysis of background adaption. In IEEE Conference on Computer Vision and Pattern Recognition, 2000, s. 503-510
  • [18] Yumiba R., Miyoshi M., Fujiyoshi H. Moving Object Detection with Background Model based on Spatio-Temporal Texture. Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV), 2011, s. 352 - 359
  • [19] Comaniciu D., Ramesh V., Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 2003, s. 564–577.
  • [20] Welch G., Bishop G. An introduction to the kalman filter. SIGGRAPH 2001, Course 8.
  • [21] Li L., Ma R., Huang W., Leman K. Evaluation of an ivs system for abandoned object detection on pets 2006 datasets. Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2006). Institute for Infocomm Research, s. 91-98, 2006.
  • [22] Smith K., Quelhas P., Gatica-Perez D. Detecting abandoned luggage items in a public space. Ninth IEEE International Workshop on Performance Evaulation of Tracking and Surveillance (PETS 2006), 2006, s. 75–82
  • [23] Auvinet E., Grossmann E., Rougier C., Dahmane M., Meunier J. Left-luggage detection using homographies and simple heuristics. Ninth IEEE International Workshop on Performance Evaulation of Tracking and Surveillance (PETS 2006), 2006, s. 51–58
  • [24] Leone A., Distante C., Buccolieri F. A shadow elimination approach in videosurveillance context, Pattern Recognition Letters, vol. 27, 2006, s. 345–355
  • [25] Forczmański P., Seweryn M. Surveillance Video Stream Analysis Using Adaptive Background Model and Object Recognition. Lecture Notes in Computer Science, Volume 6374, Computer Vision and Graphics, 2010, s. 114-121
  • [26] Hu J.-S., Su T.-M., Jeng S.-C. Robust background subtraction with shadow and highlight removal for indoor surveillance. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, s. 4545-4550
  • [27] Lou J., Yang H., Hu W., Tan T. An illumination invariant change detection algorithm. ACCV2002: The 5th Asian Conference on Computer Vision, 23--25 January 2002, Melbourne, Australia,
  • [28] Horprasert T., Harwood D., Davis L. S. A robust background subtraction and shadow detection. In Proceedings of the Asian Conference on Computer Vision, 2000
  • [29] Bascle B., Bernier O., Lemaire V. A statistical approach for learning invariants: application to image color correction and learning invariants to illumination. In International Journal of Imaging Systems and Technology, 2007
  • [30] Yokoyama M., Poggio T. A contour-based moving object detection and tracking, Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop on, 15-16 Oct. 2005, s. 271 - 276
  • [31] Cucchiara R., Grana C., Piccardi M., Prati A. Detecting Moving Objects, Ghosts and Shadows in Video Streams, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, 2003, s. 1337—1342
  • [32] CAVIAR, Context aware vision using image-based active recognition, http://homepages.inf.ed.ac.uk/rbf/CAVIAR [dostęp 09/2011]
  • [33] PETS, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, http://www.cvg.rdg.ac.uk/PETS2010/index.html [dostęp 09/2011]
  • [34] CLEAR, Classification of events, activities and relationships—evaluation campaign and workshop, http://www.clear-evaluation.org [dostęp 09/2011]
  • [35] CREDS, Call for real-time event detection solutions (creds) for enhanced security and safety in public transportation, http://www.visiowave.com/pdf/ISAProgram/CREDS.pdf [dostęp 09/2011]
  • [36] ETISEO, Video understanding evaluation, http://www-sop.inria.fr/orion/ETISEO [dostęp 09/2011]
  • [37] i-LIDS, Image library for intelligent detection systems, http://scienceandresearch.homeoffice.gov.uk/hosdb2/physical-security/detectionsystems/i-lids [dostęp 09/2011]
  • [38] SPEVI, Surveillance Performance EValuation Initiative, http://www.eecs.qmul.ac.uk/~andrea/spevi.html [dostęp 09/2011]
  • [39] CVAP, Computer Vision and Active Perception Lab at KTH in Stockholm, Recognition of human actions, http://www.nada.kth.se/cvap/actions/ [dostęp 09/2011]
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS3-0022-0078
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