Particle Markov Chain Monte Carlo for Bayesian multi-target tracking

We propose a new multi-target tracking (MTT) algorithm capable of tracking an unknown number of targets that move close and/or cross each other in a dense environment. The optimal Bayes MTT problem is formulated in the Random Finite Set framework and Particle Markov Chain Monte Carlo (PMCMC) is applied to compute the multi-target posterior distribution. The PMCMC technique is a combination of Markov chain Monte Carlo (MCMC) and sequential Monte Carlo methods to design an efficient high dimensional proposal distributions for MCMC algorithms. This technique allows our multi-target tracker to handle high track densities in a computationally feasible manner. Our simulations show that under scenarios with a large number of closely spaced tracks the estimated number of tracks and their trajectories are reliable.