The traditional mean-shift (MS) tracking algorithm lacks an effective bandwidth update strategy for surface moving targets in unmanned boats with
 anisotropic changes in contour. To address the challenge, we propose an anisotropic bandwidth-adaptive MS tracking algorithm. Firstly, the Riemann integral is used to approximate the  normalized constant Ch of the probability density of the feature sub model into the integral form.
Thus, the relationship between Ch corresponding to different scale parameters h is obtained. Secondly, the gradient ascent method is applied to make the similarity function between target model and target candidate model reach the local maximum, thereby the bandwidth and position of the target in the next frame can be estimated. Finally, two regularization parameters are introduced to correct the scale parameters to prevent the bandwidth updating from being too small or too large. Experimentsal results show that compared with the traditional MS and isotropic bandwidth-adaptive MS, the accuracy of the center location of our algorithm is increased by about 77.2% and 31.1%,   and its running speed is up to 20.7 fps, which proves its ability of anisotropic bandwidthadaptive adjustment, as well as its robustness and real-time.