An adaptive scale updating algorithm based on boundary force is presented to improve the deficiency that the kernel-bandwidth of Mean-Shift is not changeable. Based on the analysis of weighted histogram of the target feature, this paper introduces a region likelihood to extract local information of the target. Then, by comparing the region likelihood in successive frames, it constructs a boundary force to locate the boundary points of the target model and updates the bandwidth of kernel-function adaptively. The experimental results show that the proposed method improves the effect of Mean-Shift when the size or shape of target changes and satisfies the real-time request.