Seamless group target tracking using random finite sets

In many surveillance applications, target tracking algorithms have to deal with dense group targets in contrast to independently moving and well-separated targets as assumed in most scenarios. An effective strategy to handle such a group of targets is to first track the overall group and then attempt to extract the states of individual targets. Based on the Random Finite Set theory, the τ-Cardinalized Probability Hypothesis Density (τ-CPHD) filter is proposed in this paper as an effective method for group target tracking. This filter accurately extracts target states and represents the distribution of the legacy PHD. Association and track extraction are done in the post-processing step, without affecting the filtering process or overloading the computing resources. Furthermore, the group motion is modeled in conjunction with individual target motion models, with model proportion being updated by the proposed filter. Initialization process is carried out adaptively using the group motion trend through group state fitting. All these characteristics make the new filter flexible and seamlessly applicable to real-world tracking problems with group targets. Simulations demonstrate the superior performance of the proposed filter with individual target and group motion models being considered in combination or separately.