3D articulated hand tracking by nonparametric belief propagation on feasible configuration space

An efficient articulated hand tracking method underlying the 3D graphical model from monocular image sequences is proposed in this paper. Due to the inaccurate dependences among the components of human hand leading to distorted estimates in previous work, we design a pertinence graphical model combined with domain-specific heuristics among the components of human hand describing the hand's 3D structure, kinematics, and dynamics. The proposed model decomposes multivariate, joint distributions into a set of local interactions among small subsets. The modular structure provides an intuitive language for expressing domain-specific knowledge about the variable relationships, and facilitates tracking each hand component independently. And then, we provide a novel belief propagation algorithm to inference in hand graphical model. The algorithm can accommodate an extremely broad class of potential functions besides the potentials appropriate for our model. The experimental results show the robustness and efficiency of tracking each hand component.