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 MDPF (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.