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| inria-00326717, version 1 |
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| The 1st International Workshop on Machine Learning for Vision-based Motion Analysis - MLVMA'08 (2008) |
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| The abundance of video surveillance systems has created a dire need for computational methods that can assist or even replace human operators. Research in this field, however, has yet to tackle an important real-world scenario: extremely crowded scenes. The excessive amount of people and their activities in extremely crowded scenes present unique challenges to motion-based video analysis. In this paper, we present a novel statistical framework for modeling the motion pattern behavior of extremely crowded scenes. We construct a rich yet compact representation of the local spatio-temporal motion patterns and model their temporal behaviors with a novel, distribution-based Hidden Markov Model (HMM), exploiting the underlying statistical characteristics of the scene. We demonstrate that, by capturing the steady-state behavior of a scene, we can naturally detect unusual events as unlikely motion pattern variations. The experiments show promising results in extremely crowded real-world scenes with complex activities that are hard for even human observers to analyze. |
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| 1: | Department of Computer Science |
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Drexel University |
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| Domain | : | Computer Science/Computer Vision and Pattern Recognition |
| inria-00326717, version 1 | |
| http://hal.inria.fr/inria-00326717/en/ | |
| oai:hal.inria.fr:inria-00326717_v1 | |
| From: Peter Sturm | |
| Submitted on: Sunday, 5 October 2008 12:35:53 | |
| Updated on: Monday, 6 October 2008 09:42:30 | |
