Representing dense crowd patterns using bag of trajectory graphs
The aim of this paper was to address the problem of dense crowd event recognition in the surveillance video. Previous particle flow-based methods efficiently capture the convolutional motion in the crowded scene. However, the group-level description was rarely studied due to huge loss of group structure and intra-class variability. To address these issues, we present a novel crowd behavior representation called bag of trajectory graphs (BoTG). Firstly, we design a group-level representation beyond particle flow. From the observation that crowd particles are composed of atomic subgroups corresponding to informative behavior patterns, particle trajectories that simulate motion of individuals will be clustered to form groups. Secondly, we connect nodes in each group as a trajectory graph and propose 3 informative features to encode the graphs, namely, graph structure, group attribute, and dynamic motion, which characterize the structure, the motion within, and among the trajectory graphs. Finally, each clip of crowd event can be further described by BoTG as the occurrences of behavior patterns, which provides critical clues for categorizing specific crowd event. We conduct extensive experiments on public datasets for abnormality detection and event recognition. The results demonstrate the effectiveness of our BoTG on characterizing the group behaviors in dense crowd.
bag of trajectory graphs event recognition crowd behavior group attributes