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In this paper, we discuss hand modeling, constraint analysis and associated tracking. We take advantage of several correlations between joint rotations in the hand to reduce the number of degrees of freedom in the model and provide a simple and intuitive model. In the second step, an improved tracking algorithm is proposed to detect the region of the fingers. The temporal evolution of the probability density is achieved through sampling from the prior distribution, followed by local optimization of the sample positions to obtain updated modes. It is a good precondition for gesture recognition.