A generic framework for video understanding applied to group behavior recognition

Sofia Zaidenberg (1) , Bernard Boulay (1) , François Bremond (1)

1 STARS - Spatio-Temporal Activity Recognition Systems

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.

Mots clés: event detection; group tracking; behavior recognition; computer vision

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Sofia Zaidenberg, Bernard Boulay, François Bremond. A generic framework for video understanding applied to group behavior recognition. AVSS 2012 - 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, IEEE Computer Society, Sep 2012, Beijing, China. pp.136 -142, ⟨10.1109/AVSS.2012.1⟩. ⟨hal-00702179⟩