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Group context learning for event recognition

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4 Author(s)
Yimeng Zhang ; Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA ; Weina Ge ; Ming-Ching Chang ; Xiaoming Liu

We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, following, chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the group-level scenario context from automatically extracted individual trajectories. We first perform a group structure analysis to produce a weighted graph that represents the probabilistic group membership of the individuals. We then extract features from this graph to capture the motion and action contexts among the groups. The features are represented using the “bag-of-words” scheme. Finally, our method uses the learned Support Vector Machine (SVM) to classify a video segment into the six event categories. Our implementation builds upon a mature multi-camera multi-target tracking system that recognizes the group-level events involving up to 20 individuals in real-time.

Published in:

Applications of Computer Vision (WACV), 2012 IEEE Workshop on

Date of Conference:

9-11 Jan. 2012