Tracking Multiple Extended Objects with a Latent Directional Group Structure: A Nonparametric Bayesian Learning Approach

Abstract

We propose a Bayesian model and inference method for tracking multiple extended objects with a latent directional group structure. Groups of objects are defined by a common directional mode, i.e., the objects in a group move in the same direction and with the same speed up to random fluctuations. The number of directional groups, the parameters defining the groups, and the group affiliations of the objects are unknown; they are learned online, simultaneously with the tracking process. Our online learning method is based on a novel directional motion model with a Dirichlet process nonparametric prior for the group parameters, and it uses a Gibbs sampler for joint parameter estimation and object clustering. The Gibbs sampler is combined with a recently proposed extended multiobject tracking method using the belief propagation algorithm. Simulation results show that exploitation of the objects' latent directional group structure using the proposed method significantly improves the performance of extended multiobject tracking.