Online multitarget detection and tracking using sequential Monte Carlo methods

Abstract

In this paper, we present a new sequential Monte Carlo (SMC) algorithm for online joint multitarget tracking (MTT) and detection in the presence of spurious objects, e.g., clutter. The proposed method provides an efficient solution to deal with two major challenges in MIT problems: 1) time-varying number of targets, and 2) measurement-to-target association. By detecting regions of interest within the surveillance region and monitoring their appearance and disappearance, we are able to estimate the number of targets, even when the environment is hostile with low target detection probability and high clutter density. Adopting an efficient 2-D data assignment algorithm that computes all feasible assignments subject to certain constraints, we are able to efficiently and effectively marginalize the association hypotheses from the likelihood junction. Subsequently, we utilize SMC methods, also known as particle filters, to recursively and jointly estimate the multitarget states. Computer simulations and performance evaluation demonstrate the robustness of the proposed method for multitarget detection and tracking within a hostile environment in terms of high clutter density and low target detection probability.