3D model-based visual hand tracking

Capturing human hand motion through visual input is a challenging problem that involves the estimation of both global hand pose as well as the local finger articulation. This is a difficult task that requires a search in a high dimensional space due to the high degrees of freedom that fingers exhibit and the self occlusions caused by global hand motion. We propose a divide and conquer approach to estimate both global and local hand motion. The hand pose is determined from the palm using Iterative closed point (ICP) algorithm and factorization method. By incorporating natural hand motion constraints, we propose an efficient tracking algorithm based on a sequential Monte Carlo technique for tracking finger motion. Finally, the iteration step between the pose estimation and finger articulation tracking is performed in an EM fashion to obtain an accurate configuration estimation. Our experiments show that our approach is accurate and robust for natural hand movements.