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Human skin color distribution is relatively compact in a color space. That skin pixels in each frame are closed together as a Â¿dot cloudÂ¿ is hypothesized, the shape evolution of Â¿dot cloudÂ¿ in the color space from frame to frame is parameterized as translation, scaling and rotation. The linear combination of forecasts, which is consisted of 2-order Markov predictor and Wiener one step predictor, is proposed instead of single predictor to predict these parameters for the next frame which is to be segmented, and human skin biological feature is then adopted to remove camouflage noise. Extensive tests prove that this algorithm is quite sensitive to human color, and more accurate for human skin segmentation with Bayes classifier.