Dynamic bayesian networks for visual surveillance with distributed cameras

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4272 LNCS, Issue , 2006, Pages 240-243

  Zajdel, Wojciech ^a   Cemgil, A Taylan ^b   Kröse, Ben J A ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CAMERAS; PROBABILISTIC LOGICS; REAL TIME SYSTEMS; TRACKING (POSITION);

CAMERA-TO-CAMERA TRAJECTORIES; OFFICE ENVIRONMENTS; PROBABILISTIC FRAMEWORK;

COMPUTER NETWORKS;

EID: 33845249166     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11907503_22     Document Type: Conference Paper

References (6)

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2. K. Murphy. Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California, Berkeley, 2002.
3. H. Pasula, S. Russell, M. Ostland, and Y. Ritov. Tracking many objects with many sensors. In Int. Joint Conf. on Artificial Intelligence, pages 1160-1171, 1999.
4. C. E. Rasmussen. The infinite Gaussian mixture model. In Advances in Neural Information Processing Systems 12, pages 554-560, 2000.
5. W. Zajdel. Bayesian Visual Surveillance. From object detection to distributed cameras. PhD thesis, University of Amsterdam, 2006.
6. W. Zajdel, N. Vlassis, and B. J. A Kröse. Bayesian methods for tracking and localization. In E. Aarts, J. Korts, and W. Verhaegh, editors, Intelligent Algorithms, pages 243-258. Kluwer Academic Publishers, 2005.