DocumentCode :
3006754
Title :
Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models
Author :
Kratz, Louis ; Nishino, K.
Author_Institution :
Dept. of Comput. Sci., Drexel Univ., Philadelphia, PA, USA
fYear :
2009
fDate :
20-25 June 2009
Firstpage :
1446
Lastpage :
1453
Abstract :
Extremely crowded scenes present unique challenges to video analysis that cannot be addressed with conventional approaches. We present a novel statistical framework for modeling the local spatio-temporal motion pattern behavior of extremely crowded scenes. Our key insight is to exploit the dense activity of the crowded scene by modeling the rich motion patterns in local areas, effectively capturing the underlying intrinsic structure they form in the video. In other words, we model the motion variation of local space-time volumes and their spatial-temporal statistical behaviors to characterize the overall behavior of the scene. We demonstrate that by capturing the steady-state motion behavior with these spatio-temporal motion pattern models, we can naturally detect unusual activity as statistical deviations. Our experiments show that local spatio-temporal motion pattern modeling offers promising results in real-world scenes with complex activities that are hard for even human observers to analyze.
Keywords :
human factors; image motion analysis; image recognition; video signal processing; anomaly detection; extremely crowded scenes; local space-time volumes; local spatio-temporal motion pattern behavior; motion variation; rich motion patterns; spatial-temporal statistical behaviors; spatio-temporal motion pattern models; steady-state motion behavior; video analysis; Event detection; Hidden Markov models; Humans; Image motion analysis; Layout; Motion analysis; Motion detection; Pattern analysis; Ultraviolet sources; Video sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
ISSN :
1063-6919
Print_ISBN :
978-1-4244-3992-8
Type :
conf
DOI :
10.1109/CVPR.2009.5206771
Filename :
5206771
Link To Document :
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