Combining JPDA and particle filter for visual tracking

Merging and splitting of objects cause challenges for visual tracking. This is due to observation ambiguity, object lost, and tracking errors when objects are close together. In this paper, we propose a method to combine the joint probabilistic data association (JPDA) and the particle filter to maintain tracks of objects. The results of JPDA are employed to improve the observation model in the particle filter. Based on the ability of handling missing detections and clutter of JPDA, tracks of objects can be maintained after merging or splitting. Conversely, the particle filter also improves the performance of JPDA by fusing other observations such as color and background subtraction information. Hence, our method can take advantages from both JPDA and particle filter to track objects through merging and splitting.