Global stochastic optimization for robust and accurate human motion capture

Tracking of human motion in video is usually tackled either by local optimization or filtering approaches. While local optimization offers accurate estimates but often looses track due to local optima, particle filtering can recover from errors at the expense of a poor accuracy due to overestimation of noise. In this paper, we propose to embed global stochastic optimization in a tracking framework. This new optimization technique exhibits both the robustness of filtering strategies and a remarkable accuracy. We apply the optimization to an energy function that relies on silhouettes and color, as well as some prior information on physical constraints. This framework provides a general solution to markerless human motion capture since neither excessive preprocessing nor strong assumptions except of a 3D model are required. The optimization provides initialization and accurate tracking even in case of low contrast and challenging illumination. Our experimental evaluation demonstrates the large improvements obtained with this technique. It comprises a quantitative error analysis comparing the approach with local optimization, particle filtering, and a heuristic based on particle filtering