Book chapter
Spatio-Temporal Motion Pattern Models of Extremely Crowded Scenes
Machine Learning for Vision-Based Motion Analysis, pp 263-274
01 Jan 2011
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Spatio-Temporal Motion Pattern Models of Extremely Crowded Scenes
- Creators
- Louis Kratz - Laboratoire d'Informatique de Paris-NordKo Nishino - Computer Science Department [Drexel]
- Contributors
- L Wang (Editor)G Zhao (Editor)L Cheng (Editor)M Pietikainen (Editor)
- Publication Details
- Machine Learning for Vision-Based Motion Analysis, pp 263-274
- Series
- Advances in Pattern Recognition
- Publisher
- Springer Nature; GODALMING
- Number of pages
- 12
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000323582800011
- Other Identifier
- 991019168448604721
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- Web of Science research areas
- Computer Science, Artificial Intelligence