Conference proceeding
Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models
2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 1446-1453
Jun 2009
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models
- Creators
- Louis Kratz - Drexel UniversityKo Nishino - Drexel UniversityIEEE
- Publication Details
- 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 1446-1453
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000279038000185
- Other Identifier
- 991019170572304721
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- Web of Science research areas
- Computer Science, Artificial Intelligence