In order to enhance the robustness of IR target tracking under complex background

an improved Mean Shift tracking algorithm is proposed. This algorithm combines the advantage of gradient matched searching strategy based on mean shift with the robustness of the tracking algorithm based on featutes classification，and likelihood ratio weighted kernel histogram target representing model is presented. In improved model the likelihood ratio of gray level feature of target and local background is regarded as a weighted value of original kernel histogram

and then the robustness of tracking the IR target with low contrast is enhanced by increasing the shift weighted value of target gray level in the form of mean shift tracking

and background interference is suppressed effectively. Adaptive performance of the algorithm is improved via proposing the discrimination criterion of model updating based on tracking complexity estimation under target occlusion. The validity of new algorithm is verified by the actual experiment.