Learning object motion patterns for anomaly detection and improved object detection

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Basharat, Arslan ^a   Gritai, Alexei ^a   Shah, Mubarak ^a  

^a University of Central Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; COMPUTER VISION; COPYING; EDUCATION; FEATURE EXTRACTION; FINANCIAL DATA PROCESSING; IMAGE PROCESSING; LEARNING ALGORITHMS; MAGNETOSTRICTIVE DEVICES; PATTERN RECOGNITION; PIPELINES; PIXELS; RISK ASSESSMENT; SECURITY SYSTEMS;

ANOMALY DETECTIONS; CAMERA SURVEILLANCE; DESTINATION LOCATION; EVENT DETECTION; LEARNING OBJECTS; LEARNING PATTERNS; LEARNING RATES; MULTIVARIATE GAUSSIAN MIXTURE MODEL; OBJECT DETECTION; OBJECT SIZE; PIXEL LEVEL; PROBABILITY DENSITY FUNCTIONS; QUANTITATIVE ANALYSIS; SCENE MODELING; SURVEILLANCE VIDEOS; TRACKING MODULE; TRANSITION TIME;

PROBABILITY DENSITY FUNCTION;

EID: 51949114606     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587510     Document Type: Conference Paper

References (19)

1. H. Buxton. Generative Models for Learning and Understanding Dynamic Scene Activity. Workshop on GMBV, 2002.
2. R. Collins, A. Lipton, and T. Kanade. Introduction to the special section on video surveillance. IEEE Transactions on PAMI, 22(8):745-746, 2000.
3. A. Elgammal, R. D., D. H., and L. S. Davis. Background and foreground modeling using nonparametric kernel density for visual surveillance. 90(7):1151-1163, July 2002.
4. M. Figueiredo and A. Jain. Unsupervised learning of finite mixture models. PAMI, IEEE Transactions on, 2002.
5. W. Grimson, C. Stauffer, R. Romano, and L. Lee. Using adaptive tracking to classify and monitor activities in asite. CVPR, 1998.
6. M. Harville. A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models. ECCV, 2002.
7. W. Hu, X. Xiao, Z. Fu, D. Xie, and S. Maybank. A system for learning statistical motion patterns. TPAMI, 2006.
8. O. Javed and M. Shah. Tracking and object classification for automated surveillance. ECCV, 2002.
9. N. Johnson and D. Hogg. Learning the distribution of object trajectories for event recognition. BMVC, 1995.
10. I. Junejo, O. Javed, and M. Shah. Multi feature path modeling for video surveillance. ICPR, 2004.
11. J. F. Kenney and E. S. Keeping. Mathematics of Statistics. Van Nostrand, 1951.
12. D. Makris and T. Ellis. Learning semantic scene models from observing activity in visual surveillance. Systems, Man and Cybernetics, Part B, IEEE Transactions on, 2005.
13. L. Marcenaro, F. Oberti, G. F., and C. Regazzoni. Distributed architectures and logical-task decomposition in multimedia surveillance systems. Proceedings of the IEEE, 2001.
14. P. Remagnino and G. Jones. Classifying Surveillance Events from Attributes and Behaviour. BMVC, 2001.
15. I. Saleemi, K. Shafique, and M. Shah. Probabilistic modeling of scene dynamics for applications in visual surveillance. Accepted for Publication in TPAMI, 2008.
16. C. Stauffer and W. Grimson. Adaptive background mixture models for real-time tracking. CVPR, 1999.
17. C. Stauffer and W. Grimson. Learning patterns of activity using real-time tracking. PAMI, IEEE Trans. on, 2000.
18. X. Wang, K. Tieu, and E. Grimson. Learning semantic scene models by trajectory analysis. ECCV, 2006.
19. M. L. Y.-L. Tian and A. Hampapur. Robust and Efficient Foreground Analysis for Real-Time Video Surveillance. CVPR, 2005.