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arXiv · 1206.5065

A generic framework for video understanding applied to group behavior recognition

Sofia Zaidenberg·Bernard Boulay·François Bremond
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Abstract

This paper presents an approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior. This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence. First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm. A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language. The group events recognition approach is successfully validated on 4 camera views from 3 datasets: an airport, a subway, a shopping center corridor and an entrance hall.

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Sofia Zaidenberg, Bernard Boulay, François Bremond. 2012-06-22. A generic framework for video understanding applied to group behavior recognition. https://doi.org/10.1109/avss.2012.1

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