Finding group interactions in social gathering videos

Neha Bhargava, Subhasis Chaudhuri

Abstract: In this paper, we propose a framework for recognizing the group dynamics embedded in social gathering videos. We aim to identify interacting sub-groups present in a scene. In particular we are interested in identifying the participants converging to form a group, the participants dispersing from a group and the ones in static groups. We use the linear cyclic pursuit (LCP) based framework to model the collective motion. The proposed algorithm employs trajectories of individuals over a period of time to estimate the model parameters. We show that the details of group dynamics are hidden in the eigenvalues and eigenvectors of the pursuit matrix. The experiments are done on the simulated data as well as on the real gathering videos. The results show a promising scope of the proposed approach.