Efficient Mean Shift Belief Propagation for Vision Tracking

Abstract

A mechanism for efficient mean-shift belief propagation (MSBP) is introduced. The novelty of our work is to use mean-shift to perform nonparametric mode-seeking on belief surfaces generated within the belief propagation framework. Belief Propagation (BP) is a powerful solution for performing inference in graphical models. However, there is a quadratic increase in the cost of computation with respect to the size of the hidden variable space. While the recently proposed nonparametric belief propagation (NBP) has better performance in terms of speed, even for continuous hidden variable spaces, computation is still slow due to the particle filter sampling process. Our MSBP method only needs to compute a local grid of samples of the belief surface during each iteration. This approach needs a significantly smaller number of samples than NBP, reducing computation time, yet it also yields more accurate and stable solutions. The efficiency and robustness of MSBP is compared against other variants of BP on applications in multi-target tracking and 2D articulated body tracking.