JPDAF-based Pedestrian Trajectory Estimation and Combination Leveraged by the Hungarian Method in Crowded Environments

This paper presents a robust pedestrian trajectory estimation and combination leveraged by the Hungarian method in crowded environments. Traditional Bayesian filters, such as the Joint Probabilistic Data Association Filter (JPDAF), sometimes fail to track pedestrians continuously due to persistent occlusion and abrupt movements for collision avoidance. In this study, we have improved the success rate of JPDAF-based pedestrian tracking by combining broken trajectories using the Hungarian method. To validate our approach, we conducted multiple-pedestrian tracking experiments in a crowded environment observed by two LiDARs. We confirmed that, even when JPDAF lost track of the target due to frequent occlusion, the proposed method reflecting the direction of the pedestrian flow improved the tracking success rate substantially by reconnecting isolated trajectories.