经典Mean Shift跟踪算法使用单一颜色直方图跟踪目标，导致其对目标外观的变化鲁棒性较差。为了解决该问题，提出一种多颜色直方图自适应组合Mean Shift跟踪算法。该算法利用多个视图的颜色核函数直方图的加权组合作为目标模型进行Mean Shift跟踪；为了适应目标外观的变化，利用目标区域对每一颜色直方图的概率图均值和方差的比值评价每一颜色直方图的可靠性，并自适应地计算其组合权值。实验结果表明，与现有Mean Shift跟踪算法相比，提出的跟踪算法对目标的外观变化具有更强的鲁棒性。

The traditional Mean Shift tracker with single color histogram result in aborting under changes of appearance of object. To deal with this problem, a Mean Shift tracking algorithm using multiple color histograms adaptive integration is proposed. The proposed algorithm enhances the Mean Shift tracker with multiple reference color histograms obtained from different target views, and takes the weighted integration of these histograms as the target model. To adapt to changes of appearance of objects, the proposed algorithm dynamically assesses the reliability of each color histogram and adaptively computes the color’s fusion weight by the ratios of the mean and variance of the probability image of the object. Experimental results show that the proposed Mean Shift tracking algorithm is superior over the existing Mean Shift tracking algorithm of appearance of an object is changing.