Tracking multiple interacting targets using a joint probabilistic Data Association filter

In this paper, we describe and evaluate an original Monte Carlo JPDAF for tracking interacting autonomous targets in a cluttered environment. The originality of the proposed algorithm consists in reducing the complexity of the prediction step by selecting and separately updating groups of targets in interaction. The complexity of the correction step is addressed by Data Association and a gating procedure as found in literature. The main assumptions we make in this paper are (i) that the evolution of the state of each target only depends on the states of all the targets at the previous time step and (ii) that a generic simulator or a function modeling the targets' behaviors and their mutual interactions is available. We also build an approximate interaction graph between targets on the fly on the basis of simple information like their location, as it has been done in previous work. Experiments show that representing interactions this way can lead to good tracking efficiency with low computational cost.