SMC-PHD-based multi-target tracking with reduced peak extraction

Abstract

The Probability Hypothesis Density (PHD) filter is a powerful new tool in the field of multitarget tracking. Unlike classical multi-target tracking approaches, such as Multiple Hypothesis Tracking (MHT), in each scan it provides a complete solution to multi-target state estimation without the necessity for explicit measurement-to-track data association. The PHD filter recursively propagates the first order moment of the multi-target posterior. This allows us to determine the expected number of targets as well as their state estimates at each scan. However, there is no implicit connection between the target state estimates in consecutive scans. In this paper, a new cluster-based approach is proposed for track labeling in the Sequential Monte Carlo (SMC i.e. particle filter based) PHD filter. The method associates a likelihood vector to each particle in the SMC estimate. This vector indicates the likelihood that the particle estimate belongs to each of the established target tracks. This likelihood vector is propagated along with the PHD moment and updated with the PHD function. By maintaining a set of associations from scan to scan, the new method provides a complete PHD solution for a multi-target tracking application over time. The method is tested on both clean and noisy multi-target tracking scenarios and the results are compared to some previously published methods.