Hand motion tracking using MDPF method

Hand motion tracking is a challenging problem due to the complexity of searching in a high dimensional configuration space for an optimal estimate. This paper represents the hand feasible configurations as a discrete space, which avoids learning to find parameters as general configuration space representations do, meanwhile, it arrange the discrete data on the KD-tree which supports fast nearest neighbor retrieval and it is easy to be modified when new samples are embedded. To track hand motion efficiently, this paper presents a AMPF (Multi-Directional search with Particle Filter) algorithm, in which a 'global' optimization and a 'local' optimization are combined to obtain the best matching configuration. The 'local' method, which is designed to run in multiple processors, could choose more representative samples for global efficiently, and the global method guards the tracking process towards a global minimum. The Experiment results show that this approach is robust and efficient for tracking 3D hand motion.