Improved mean shift algorithm with multi-cue integration and histogram intersection

The mean shift tracker is commonly used in realtime target tracking. However, the original mean shift tracker employs only color feature and uses the Bhattacharya coefficient as similarity measure, resulting in low tracking accuracy. This paper proposed a novel tracking algorithm, which integrated color and texture features and employed histogram intersection and Powell´s method to track. Firstly, texture feature was extracted by the Local Binary Pattern texture operator and integrated with color feature adaptively. Log-likelihood ratio histogram was proposed to represent objects instead of histogram. Then, the rough location of the target was obtained by the mean shift algorithm based on the two features. Finally, histogram intersection was defined as the similarity metric between the target model and candidates and iteratively maximized by Powell´s method. Experimental results demonstrate the proposed method can track targets more accurately and fast.