Modeling the dynamics of individual behaviors for group detection in crowds using low-level features

This paper introduces two novel algorithms for detecting groups of people standing or freely moving in a crowded environment. The proposed algorithms exploit low-level features extracted from videos. The first algorithm, the Link Method, uses a learning and forgetting strategy for modeling dynamics of proxemics between individuals. Two versions of this algorithm are proposed: they differ in the analysis of proxemics. The second one, called Interpersonal Synchrony Method, explicitly adopts interpersonal synchrony to refine clusters of persons detected by combining together proxemics and 2D field of view of individuals. The algorithms are evaluated on both simulated and real-world video sequences from state-of-the-art databases. Clustering metrics such as the Adjusted Mutual Information shows that our models outperform the approach based on F-formations. This work developed algorithms that can be readily applied in robotics, to allow robots to automatically detect groups in crowded environments.