Kai-Wen Cheng
ABSTRACT
This paper presents a novel framework for anomaly event detection and localization in crowded scenes. We propose an anomaly detector that extends the Bayes classifier from multi-class to one-class classification to characterize normal events. We also propose a localization scheme for anomaly localization as a maximum subsequence problem in a video sequence. The maximum subsequence algorithm locates an anomaly event by discovering the optimal collection of successive patches with spatial proximity over time without prior knowledge of the size, start and end of the anomaly event. Our localization scheme can locate multiple occurrences of abnormal events in spite of noise. Experimental results on the well-established UCSD dataset show that the proposed framework significantly outperforms state-of-the-art methods up to 53.55% localization rate. This study concludes that the localization framework plays an important role in abnormal event detection.
- R. Mehran, A. Oyama, and M. Shah, "Abnormal crowd behavior detection using social force model," in CVPR, Jun. 2009, pp. 935--942.Google Scholar
- V. Mahadevan, V. B. W. Li, and N. Vasconcelos, "Anomaly detection in crowded scenes," in CVPR, Jun. 2010, pp. 1975--1981.Google Scholar
- Y. Cong, J. Yuan, and J. Liu, "Sparse reconstruction cost for abnormal event detection," in CVPR, Jun. 2011, pp. 3449--3456. Google ScholarDigital Library
- 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., vol. 30, pp. 555--560, Jan. 2008. Google ScholarDigital Library
- Y. Benezeth, P. Jodoin, V. Saligrama, , and C. Rosenberger, "Abnormal events detection based on spatio-temporal co-occurences," in CVPR, Jun. 2009, pp. 2458 -- 2465.Google Scholar
- V. Saligrama and Z. Chen, "Video anomaly detection based on local statistical aggregates," in CVPR, Jun. 2012, pp. 2112 -- 2119. Google ScholarDigital Library
- D. Helbing and P. MolnaI Ar, "Social force model for pedestrian dynamics," Physical Review E, vol. 51, pp. 4282--4286, May 1995.Google ScholarCross Ref
- A. Chan and N. Vasconcelos, "Modeling, clustering, and segmenting video with mixtures of dynamic textures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, pp. 909 -- 926, May 2008. Google ScholarDigital Library
- C. H. Lampert, M. B. Blaschko, and T. Hofmann, "Beyond sliding windows: Object localization by efficient subwindow search," in CVPR, Jun. 2008, pp. 1--8.Google Scholar
- J. Yuan, Z. Liu, and Y. Wu, "Discriminative subvolume search for efficient action detection," in CVPR, Jun. 2009, pp. 2442 -- 2449.Google Scholar
- D. Tran and J. Yuan, "Optimal spatio-temporal path discovery for video event detection," in CVPR, Jun. 2011, pp. 3321 -- 3328. Google ScholarDigital Library
- B. Scholkopt, J. Platt, J. Shawe-Taylor, A. Smola, and R. Williamson, "Estimating the support of a high-dimensional distribution," Neural Computation, vol. 13, pp. 1443--1471, Jul. 2001. Google ScholarDigital Library
- D. G. Lowe, "Distinctive image features from scale-invariant keypoints," IJCV, vol. 60, pp. 91--110, Nov. 2004. Google ScholarDigital Library
- N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in CVPR, Jun. 2005, pp. 886--893. Google ScholarDigital Library
- N. Dalal, B. Triggs, and C. Schmid, "Human detection using oriented histograms of flow and appearance," in ECCV, May 2006, pp. 428--441. Google ScholarDigital Library
Index Terms
- Abnormal crowd behavior detection and localization using maximum sub-sequence search
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