Enhanced mean shift tracking algorithm based on evolutive asymmetric kernel

Aimed at the defect of the traditional mean shift tracking algorithm which using symmetric kernel function who contains amounts of background pixels, this paper presents an enhanced mean shift tracking algorithm based on evolutive asymmetric kernel to improve the tracking accuracy and stability. The paper firstly described the calculation method of Template Center which is the key issue in introducing asymmetric kernel function into mean shift algorithm framework. Then, to combine the expression and evolution of asymmetric kernel function, level set contour evolution algorithm using regional similarity is presented. Finally, the asymmetric kernel function update strategy is introduced. The above three points constitute the mean shift tracking algorithm based on active asymmetric kernel function overall context. Experimental results show that compared to existing methods, the mean shift tracking algorithm based on evolutive asymmetric kernel presented in this paper has higher accuracy and reliability, as well as meets the real-time requirements of general tracking tasks.