An improved Mean Shift tracking algorithm based on color and texture feature

Abstract

This paper presents an improved Mean Shift tracking algorithm. It extends the classic Mean Shift tracking algorithm by combining color and texture features. In the proposed method, firstly, both the color feature and the texture feature of the target are extracted from first frame and the histogram of each feature is computed. Then the Mean Shift algorithm is run for maximizing the similarity measure of each feature independently. In last step, center of the target in the new frame is computed through the integration of the outputs of Mean Shift. Experiments show that the proposed Mean-Shift tracking algorithm combining color and texture features provides more reliable performance than single features tracking.