As mean shift algorithm based on KDE has good performance of real-time,it has been widely used in target tracking.However,the tracking robust of traditional mean shift algorithm is often depended on such features like color,etc.Moreover,the tracked position is usually affected by the scale change of target during tracking procedures.To overcome these disadvantages,a new infrared pedestrian target tracking approach based on mean shift with boundary constraint is proposed.This method uses the gradient of infrared image processed by anisotropic diffusion.Particularly,the target boundary is obtained by its gradient as well as brightness information,kernel bandwidth is adaptively adjusted.At last,the infrared pedestrian target tracking is carried out by the strategy of mean shift algorithm,and the experimental results show that the proposed approach can achieve efficient tracking when the scale of the target changes.