Unsupervised abnormal crowd activity detection using semiparametric scan statistic

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2013, Pages 767-774

  Hu, Yang ^a   Zhang, Yangmuzi ^a   Davis, Larry S ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABNORMAL ACTIVITY DETECTION; ACTIVITY DETECTION; LIKELIHOOD RATIO TESTS; SCANNING ALGORITHMS; SEARCH COMPLEXITY; SLIDING WINDOW-BASED; STATE-OF-THE-ART METHODS; UNSUPERVISED METHOD;

PATTERN RECOGNITION; STATISTICAL TESTS;

ALGORITHMS;

EID: 84884972101     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2013.115     Document Type: Conference Paper

References (23)

1. A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz. Robust real-time unusual event detection using multiple fixedlocation monitors. PAMI, 30:555-560, 2008.
2. M. Bertini, A. D. Bimbo, and L. Seidenari. Multi-scale and real-time non-parametric approach for anomaly detection and localization. CVIU, 116:320-329, 2012.
3. O. Boiman and M. Irani. Detecting irregularities in images and in video. In ICCV, 2005.
4. M. D. Breitenstein, H. Grabner, and L. V. Gool. Hunting nessie-real-time abnormality detection from webcams. In IEEE Int. Workshop on Visual Surveillance, 2009.
5. Y. Cong, J. Yuan, and J. Liu. Sparse reconstruction cost for abnormal event detection. In CVPR, 2011.
6. X. Cui, Q. Liu, M. Gao, and D. Metaxas. Abnormal detection using interaction energy potentials. In CVPR, 2011.
7. K. Fokianos, J. Qin, B. Kedem, and D. A. Short. A semiparametric approach to the one-way layout. Technometrics, 43(1):56-65, 2001.
8. A. Hendel, D. Weinshall, and S. Peleg. Identifying surprising events in videos using bayesian topic models. In ACCV, 2010.
9. http://mha.cs.umn.edu/Movies/Crowd Activity-All.avi.
10. Y. Ito, K. Kitani, J. A. D. Bagnell, and M. Hebert. Detecting interesting events using unsupervised density ratio estimation. In ECCV Workshop, 2012.
11. B. Kedem and S. Wen. Semi-parametric cluster detection. Journal of statistical theory and practice, 1(1):49-72, 2007.
12. J. Kim and K. Grauman. Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates. In CVPR, 2009.
13. M. Kulldorff. A spatial scan statistic. Communications in Statistics-Theory and Methods, 26(6):1481-1496, 1997.
14. C. Liu. Beyond pixels: Exploring new representations and applications for motion analysis. Doctoral Thesis., 2009.
15. V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos. Anomaly detection in crowded scenes. In CVPR, 2010.
16. R. Mehran, B. E. Moore, and M. Shah. A streakline representation of flow in crowded scenes. In ECCV, 2010.
17. R. Mehran, A. Oyama, and M. Shah. Abnormal crowd behavior detection using social force model. In CVPR, 2009.
18. J. Qin and B. Zhang. A goodness-of-fit test for logistic regression models based on case-control data. Biometrika, 84(3):609-618, 1997.
19. X. Wang, X. Ma, and E. Grimson. Unsupervised activity perception by hierarchical bayesian models. In CVPR, 2007.
20. S. Wu, B. E. Moore, and M. Shal. Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In CVPR, 2010.
21. T. Xiang and S. Gong. Video behavior profiling for anomaly detection. PAMI, 30:893-908, 2008.
22. B. Zhao, L. Fei-Fei, and E. P.Xing. Online detection of unusual events in videos via dynamic sparse coding. In CVPR, 2011.
23. H. Zhong, J. Shi, and M. Visontai. Detecting unusual activity in video. In CVPR, 2004.