Exploiting Pedestrian Interaction via Global Optimization and Social Behaviors

Abstract

Abstract. Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Tracking methods mostly work with the assumption that each pedestrian moves independently unaware of the objects or the other pedestrians around it. In the real world though, it is clear that when walking in a crowd, pedestrians try to avoid collisions, keep a close distance to a group of friends or avoid static obstacles in the scene. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, including social and grouping behavior as a physical model within the tracking system, and second, using a global optimization scheme which takes into account all trajectories and all frames to solve the data association problem . Results are presented on three challenging publicly available datasets, showing our method outperforms state-of-the-art tracking systems. We also make a thorough analysis of the effect of the parameters of the proposed tracker as well as its robustness against noise, outliers and missing data.

Real-time Crowd Tracking using Parameter Optimized Mixture of Motion Models

“…Zamri et al [13] used generalized minimum clique graphs for multipleperson tracking. Leal-Taixé et al [14] used a social and grouping behavior as a physical model in their tracking system. Burgos-Artizzu et al [5] presented a novel method for analyzing social behavior, particularly in mice videos, where the continuous videos are segmented into action 'bouts' by building a temporal context model.…”