Meta-tracking and Dominant Motion Patterns at the Macroscopic Crowd Level

This paper presents a method for crowd motion segmentation and generating dominant motion patterns at the macroscopic crowd level, where a crowd is treated as an entity. In this approach, the dominant motion patterns, as a base for behaviour analysis of a mass of people, are the focus of interest. Dominant motion patterns are generated based on meta-trajectories. A meta-trajectory is defined as a set of tracklets and/or trajectories of entities in the crowd. The entities are particles initially organized as a uniform grid which is overlaid on a flow field. To estimate the flow field, a dense optical flow is used. Based on advection of the particles, tracklets/trajectories are obtained. They are grouped by a graph-based clustering algorithm and meta-trajectories are obtained. By overlapping meta-trajectories with the quantized orientation of the average optical flow field dominant motion patterns are obtained. The preliminary experimental results of the proposed method are given for a subset of UCF dataset, a subset of Crowd Saliency Detection dataset, our own FER dataset and computer crowd simulation videos of characteristic behaviour.