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Spatio-Temporal Motion Pattern Models of Extremely Crowded Scenes

  • Louis Kratz
  • Ko Nishino
Part of the Advances in Pattern Recognition book series (ACVPR)

Abstract

Extremely crowded scenes present unique challenges to motion-based video analysis due to the large quantity of pedestrians within the scene and the frequent occlusions they produce. The movement of pedestrians, however, collectively form a spatially and temporally structured pattern in the motion of the crowd. In this work, we present a novel statistical framework for modeling this structured pattern, or steady-state, of the motion in extremely crowded scenes. Our key insight is to model the motion of the crowd by the spatial and temporal variations of local spatio-temporal motion patterns exhibited by pedestrians within the scene. We divide the video into local spatio-temporal sub-volumes and represent the movement through each sub-volume with a local spatio-temporal motion pattern. We then derive a novel, distribution-based hidden Markov model to encode the temporal variations of local spatio-temporal motion patterns. We demonstrate that by capturing the steady-state of the motion within the scene, we can naturally detect unusual activities as statistical deviations in videos with complex activities that are hard for even human observers to analyze.

Keywords

Optical Flow True Positive Rate Training Video Crowded Scene Query Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported in part by Nippon Telegraph and Telephone Corporation and the National Science Foundation grants IIS-0746717 and IIS-0803670.

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© Springer-Verlag London Limited 2011

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