MCMC-based particle filtering for tracking a variable number of interacting targets

TLDR

The study proposes a particle filter designed to handle interacting targets whose behavior is influenced by proximity or other targets. The filter incorporates a Markov random field motion prior to preserve target identities, replaces traditional importance sampling with an MCMC step for efficiency, and extends to variable‑number targets by adding an interaction factor to particle weights. Although the initial MRF‑based filter was computationally prohibitive for many targets, the MCMC‑based version achieves efficient and effective tracking of interacting targets, as shown by qualitative and quantitative experiments.

Abstract

We describe a particle filter that effectively deals with interacting targets--targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.