Track fusion with feedback for local trackers using MHT

With current processing power, Multiple Hypothesis Tracking (MHT) becomes a feasible and powerful solution; however a good hypothesis pruning method is mandatory for efficient implementation. The availability of a continuously increasing number of tracking systems raises interest in combining information from these systems. The purpose of this paper is to propose a method of information fusion for such trackers that use MHT locally with local information sent in the form of sensor global hypotheses and the fusion center combining them into fused global hypotheses. The information extracted from the best fused global hypotheses, in the form of ranking of received sensor global hypotheses, is sent back to local trackers, for optimized pruning. Details of the method, in terms of sensor global hypotheses generation, evaluation, pruning at local sensors, association and fusion of sensor global hypotheses at fusion center, and usage of the information received as feedback from the fusion center are presented.