Online dominant and anomalous behavior detection in videos

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2013, Pages 2611-2618

  Roshtkhari, Mehrsan Javan ^a   Levine, Martin D ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

Anomaly detection; Bag of video words; Behavior learning; Contextual information; Hierarchical scene modeling; Spatio Temporal compositions; Surveillance; Video parsing

Indexed keywords

ANOMALY DETECTION; BAG OF VIDEO WORDS; BEHAVIOR LEARNING; CONTEXTUAL INFORMATION; SCENE MODEL; SPATIO-TEMPORAL; VIDEO PARSING;

PATTERN RECOGNITION; PIXELS; SPACE SURVEILLANCE;

SECURITY SYSTEMS;

EID: 84887384449     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.337     Document Type: Conference Paper

References (30)

1. A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell., 30(3):555-560, 2008.
2. Y. Benezeth, P.-M. Jodoin, and V. Saligrama. Abnormality detection using low-level co-occurring events. Pattern Recogn. Lett., 32(3):423-431, 2011.
3. Y. Benezeth, P. M. Jodoin, V. Saligrama, and C. Rosenberger. Abnormal events detection based on spatio-temporal co-occurences. In CVPR, pages 2458-2465, 2009.
4. M. Bertini, A. Del Bimbo, and L. Seidenari. Multi-scale and realtime non-parametric approach for anomaly detection and localization. Compt. Vis. Image Und., 116(3):320-329, 2012.
5. C. M. Bishop. Pattern recognition and machine learning. Springer, New York, 2006.
6. O. Boiman and M. Irani. Detecting irregularities in images and in video. Int. J. Comput. Vision, 74(1):17-31, 2007.
7. Y. Cong, J. Yuan, and J. Liu. Sparse reconstruction cost for abnormal event detection. In CVPR, pages 3449-3456, 2011.
8. E. B. Ermis, V. Saligrama, P. M. Jodoin, and J. Konrad. Motion segmentation and abnormal behavior detection via behavior clustering. In ICIP, pages 769-772, 2008.
9. T. C. Havens, J. C. Bezdek, C. Leckie, L. O. Hall, and M. Palaniswami. Fuzzy c-means for very large data. IEEE Trans. Fuzzy Syst., PP(99):1-1, 2012.
10. P. Hore, L. Hall, D. Goldgof, Y. Gu, A. Maudsley, and A. Darkazanli. A scalable framework for segmenting magnetic resonance images. Journal of Signal Processing Systems, 54(1):183-203, 2009.
11. T. Hospedales, S. Gong, and T. Xiang. Video behaviour mining using a dynamic topic model. Int. J. Comput. Vision, pages 1-21, 2012.
12. T. M. Hospedales, L. Jian, G. Shaogang, and X. Tao. Identifying rare and subtle behaviors: A weakly supervised joint topic model. IEEE Trans. Pattern Anal. Mach. Intell., 33(12):2451-2464, 2011.
13. P. Jodoin, V. Saligrama, and J. Konrad. Behavior subtraction. IEEE Trans. Image. Proc., 21(9):4244-4255, 2012.
14. P. M. Jodoin, J. Konrad, and V. Saligrama. Modeling background activity for behavior subtraction. In Int. Conf. Distributed Smart Cameras, pages 1-10, 2008.
15. J. Kim and K. Grauman. Observe locally, infer globally: A spacetime mrf for detecting abnormal activities with incremental updates. In CVPR, pages 2921-2928, 2009.
16. K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis. Real-time foregroundbackground segmentation using codebook model. Real-Time Imaging, 11(3):172-185, 2005.
17. J. Li, S. Gong, and T. Xiang. Learning behavioural context. Int. J. Comput. Vision, 97(3):276-304, 2012.
18. V. Mahadevan, L. Weixin, V. Bhalodia, and N. Vasconcelos. Anomaly detection in crowded scenes. In CVPR, pages 1975-1981, 2010.
19. A. Mittal, A. Monnet, and N. Paragios. Scene modeling and change detection in dynamic scenes: A subspace approach. Compt. Vis. Image Und., 113(1):63-79, 2009.
20. B. T. Morris and M. M. Trivedi. Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach. IEEE Trans. Pattern Anal. Mach. Intell., 33(11):2287-2301, 2011.
21. A. Oikonomopoulos, I. Patras, and M. Pantic. Spatiotemporal localization and categorization of human actions in unsegmented image sequences. IEEE Trans. Image Process., 20(4):1126-1140, 2011.
22. K. Ouivirach, S. Gharti, and M. N. Dailey. Incremental behavior modeling and suspicious activity detection. Pattern Recognition, 46(3):671-680, 2013.
23. V. Reddy, C. Sanderson, and B. C. Lovell. Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture. In CVPR Workshops, pages 55-61, 2011.
24. E. Ricci, G. Zen, N. Sebe, and S. Messelodi. A prototype learning framework using emd: Application to complex scenes analysis. IEEE Trans. Pattern Anal. Mach. Intell., PP(99):1-1, 2012.
25. M. J. Roshtkhari and M. D. Levine. A multi-scale hierarchical codebook method for human action recognition in videos using a single example. In Conf. Computer and Robot Vision, pages 182-189, 2012.
26. V. Saligrama and C. Zhu. Video anomaly detection based on local statistical aggregates. In CVPR, pages 2112-2119, 2012.
27. P. Scovanner, S. Ali, and M. Shah. A 3-dimensional sift descriptor and its application to action recognition. In International conference on Multimedia, pages 357-360, Augsburg, Germany, 2007. ACM.
28. A. Zaharescu and R. Wildes. Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing. In ECCV, pages 563-576, 2010.
29. X. Zhu and Z. Liu. Human behavior clustering for anomaly detection. Frontiers of Computer Science in China, 5(3):279-289, 2011.
30. Z. Zivkovic. Improved adaptive gaussian mixture model for background subtraction. In ICPR, pages 28-31, 2004.