In this paper,a new mean-shift target tracking algorithm is proposed to improve the kernel function bandwidth-adaptive ability of the traditional one. First,the probability density of the eigenvalue of the target color is derived by employing the kernel function. Next,a distribution image of the target probability density is projected on the new optimal location of the target in the current video frame. Then,according to the zeroth-order moment of
the probability density distribution,the width of the tracking window in the next frame is adjusted. Thus,the adaptive bandwidth of kernel function is achieved. Finally,the ellipse parameters derived by means of the moment operation are adopted to lock the tracking target,thus achieving the target position in space,scale and direction in a complex background. Face-tracking experimental results show that,as compared with the conventional algorithm,the proposed one can achieve real-time scaling and locking of the target and estimate the target attitude,and that,it is superior to the Cam Shift algorithm in terms of resistance to the interference of similar color.