Exclusive Association Sampling to Improve Bayesian Multi-Target Tracking

Abstract

Multi-target tracking has been studied for many years, yet it remains a challenging problem, particularly in terms of implementing data association when tracking targets over several time steps. To achieve robustness, probabilistic approaches have been proposed, including Bayesian multi-target tracking methods. However, these approaches involve high calculation costs, which are incompatible with real-time applications. We propose exclusive association sampling (EAS) to improve the efficiency of Bayesian multi-target tracking methods. Although EAS is a simple procedure composed of random sorting and sampling based on the observation probability, it can be employed to increase the efficiency of association candidate generation and the calculation of statistical values. In this study, we proposed two Bayesian multi-target tracking methods based on EAS: stochastic joint probabilistic data association (SJPDA) and Rao-Blackwellized particle filter with EAS (RBPF with EAS). Evaluation of these methods with simulated data shows that integrating EAS into these methods can enhance their speed and accuracy. Moreover, evaluation on open datasets used for pedestrian tracking on camera sequences shows that the proposed methods achieve significantly better performance on some important metrics compared with representative methods.