Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking

Abstract

An efficient Nonparametric Belief Propagation (NBP) algorithm is developed in this paper. While the recently proposed nonparametric belief propagation algorithm has wide applications such as articulated tracking [22, 19], superresolution [6], stereo vision and sensor calibration [10], the hardcore of the algorithm requires repeatedly sampling from products of mixture of Gaussians, which makes the algorithm computationally very expensive. To avoid the slow sampling process, we applied mixture Gaussian density approximation by mode propagation and kernel fitting [2, 7]. The products of mixture of Gaussians are approximated accurately by just a few mode propagation and kernel fitting steps, while the sampling method (e.g. Gibbs sampler) needs many samples to achieve similar approximation results. The proposed algorithm is then applied to articulated body tracking for several scenarios. The experimental results show the robustness and the efficiency of the proposed algorithm. The proposed efficient NBP algorithm also has potentials in other applications mentioned above.