The Social Force PHD Filter for Tracking Pedestrians.

This paper addresses the problem of tracking multiple pedestrians whose motion is dependent on one another. The behavior of a pedestrian may be often affected by the motion of other pedestrians, obstacles in the surrounding, and his/her intended destination. Hence, a motion modeling technique, which integrates the various factors that affect the motion of pedestrians, is needed. In this paper, a social force based motion model integrated into the probability hypothesis density (PHD) framework is proposed. The social force concept has previously been used to model pedestrian motion when there are interactions among pedestrians. In this paper, the sequential Monte Carlo (SMC) technique and the Gaussian mixture (GM) technique are used to implement the proposed Social Force PHD (SF-PHD) filter and its multiple model variant in pedestrian tracking scenarios. A particle labeling approach is used in the SMC technique while a Gaussian component labeling approach is used in the GM technique for this purpose. Also, a modified performance measure independent of the proposed approaches but based on the posterior Cramer-Rao lower bound for targets whose motion is dependent on one another is derived. Simulation and real data-based results show that both the SMC implementation and the GM implementation of the proposed SF-PHD filter outperform existing filters that assume independent motion among ground targets.