Group context learning for event recognition

Abstract

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 multitarget tracking system that recognizes the group-level events involving up to 20 individuals in real-time.

A Comprehensive Review of Group Activity Recognition in Videos

“…Zhou et al [39] designed a set of features which measures the strength of causality between two trajectories and another set describes the type of causality. The two sets of features along with conventional velocity and position features of a trajectory-pair are fused to explore the relationship between two object entities.…”