Hand tracking is a challenging problem due to the complexity of searching in a 20+ degrees of freedom (DOF) space for an optimal estimation of hand configuration. The feasible hand configurations are represented as a discrete space, which avoids learning to find parameters as general configuration space representations do. Then, an extended simulated annealing method with particle filtering to search for optimal hand configuration in the proposed discrete space, in which simplex search running in multi-processor is designed to predict the hand motion instead of initializing the simulated annealing randomly, and particle filtering is employed to represent the state of the tracker at each layer for searching in high dimensional configuration space. Experimental results show that the proposed method makes the hand tracking more efficient and robust.
