MAP Particle Selection in Shape-Based Object Tracking

Abstract

The Bayesian filtering for recursive state estimation and the shape-based matching methods are two of the most commonly used approaches for target tracking. The multiple hypothesis shape-based tracking (MHST) algorithm, proposed by the authors in a previous work, combines these two techniques using the particle filter algorithm. The state of the object is represented by a vector of the target corners (i.e. points in the image with high curvature) and the multiple state configurations (particles) are propagated in time with a weight associated to their probability. In this paper we demonstrate that, in the MHST, the likelihood probability used to update the weights is equivalent to the voting mechanism for generalized Hough transform (GHT)-based tracking. This statement gives an evident explanation about the suitability of a MAP (maximum a posteriori) estimate from the posterior probability obtained using MHST. The validity of the assertion is verified on real sequences showing the differences between the MAP and the MMSE estimate.