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.