Abstract: To implement the hand tracking and contour tracking accurately and quickly in complex background, a novel algorithm for hand tracking with particle filter and skin color adaptive gradient vector flow snake (GVF snake) model is proposed. This algorithm especially applies to the extraction of deep concave region of hand contour, and overcomes the problem that particle filter can not obtain accurate information. Firstly, the hand region obtained by the particle filter is grayscale enhanced by skin color, weakening the background gradient information. And then the GVF snake model, with adaptive gradient vector flow field and adaptive external guidance force improved by skin color, is introduced to extract the real contour and modify the systematic observation and system state of particle filter. In this case, it can reduce the possibility of degradation of particles and realize hand tracking more accurately. The experimental results show that the proposed algorithm extracts more accurate contour of hand and improves real-time performance of human tracking by 13% and the root mean square error is reduced by 48%, under the conditions of complexity, moving background and even a wide range of occlusion.

Key words: hand tracking, hand contour, gradient vector flow snake (GVF snake) model, adaptiveness, particle filter