Target tracking technology has good application prospect at present, but in an embedded processing platform is facing complicated high real-time requirements, tracking, etc., and restricted by cost and embedded processing platform to calculate force, it is often difficult to meet the demand of reality of its processing effect, so the image processing technology such as target tracking ground implementation is a hotspot of current research content. To solve this problem, this paper implemented an improved Mean Shift target tracking algorithm on FPGA platform. The algorithm first searched for the target by the gradient climbing of the probability density distribution of the target, and then used the prediction mechanism of Kalman filter to predict the location of the next frame search calculation, so as to reduce the number of iterations of Mean Shift. The algorithm makes full use of the parallel and pipelined-processing characteristics of FPGA to realize the real-time target tracking in 1 920×1 080@60 Hz hd video image scene, and the Kalman filtering algorithm enables it to have a certain ability to resist occlusion interference in more complex scenes.