Global space-time association for Probability Hypothesis Density filter

The Probability Hypothesis Density (PHD) method can handle multi-target tracking problem, but it needs a specific association method to extract the target tracks. Up to now, such association methods are limited in the scope of temporal association, for example, the track labeling method. In this paper, we present the concept of the consistency measure between any two local peaks at the adjacent two time instants by using both spatial structure information and temporal evolution information. Furthermore, the global-space-time association is proposed through extracting the tracks one-by-one based on the consistency measure and three rules. The proposed method is testified via a simulation comparison with the track labeling method.