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In order to accurately track Ochotona curzoniae in natural habitat environment, an object tracking method based on Meanshift algorithm was proposed. Considering the object tracking method of kernel Meanshift algorithm based on RGB color histogram usually has the deformation of inaccurate tracking or lose of target in the scenario that the color is similar between the background and the object. In view of the problem that the color between the Ochotona curzoniae and the background is similar in the object tracking process in natural habitat environment, a visual descriptor named as the local texture difference operator (LTDC) was proposed to reflect the subtle differences between the Ochotona curzoniae and background. The LTDC operator was combined with color information to characterize the object model and the object model was embedded into the Meanshift tracking framework for the object tracking of Ochotona curzoniae. The experimental results show that the proposed method for characterizing the object has a strong difference ability of target and background. The object can be accurately positioned in the color similar scenario of the object and the background. Compared with the FLBP algorithm, the average iteration number of proposed method is 79.04% of the average iteration number of the FLBP algorithm, the average tracking total time of proposed method is 8235% of the average tracking total time of the FLBP algorithm, the average tracking speed of proposed method is 1.22 times of the average tracking speed of the FLBP algorithm.