Abstract: Hand tracking is key to realize natural human-computer interaction.The existing hand tracking algorithms for human-computer interaction can be easily affected by illumination and have poor robustness.In this paper,an adaptive hand tracking by the fusion of depth and skin color features was put forward.Considering the deformation of hand,the algorithm uses the depth threshold to achieve adaptive changes of tracking region with the continuity and smoothness of depth feature and obtains the hand alternative regions.Then,via skin color feature of YCbCr space,the skin normalized histograms were established to describe the alternative regions.Under the framework of particle filter,the hand tracking is converted into Bayes estimation problem and the algorithm determines the hand position based on the maximum a posterior (MAP).The variance of particle weights was monitored to solve the problem of tracking failure.Experimental results show that,the proposed algorithm can achieve accurate and robust tracking performance in real-time with complex background.

Key words: Depth feature,Particle filter,Adaptive hand tracking,Skin normalized histogram