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In the process of target tracking, the CamShift algorithm only uses the color information of the target to achive tracking, and the complex background such as similar objects, occlusion, the interference of light and so on, which would lead to the divergence of the target search window and reduce tracking performance. In order to solve the defect of the CamShift algorithm, a method of target tracking based on threshold value judgement was proposed. The method combined the OTSU algorithm with the Snake model which used OTSU to segment the image with the best threshold to separate the foreground and background, then the initial contour of the target was extracted that was taken as the input contour of the Snake model, and the precise contour of the target would generate. And the target centroid could be calculated by using the pixels in the minimum rectangle region that outsided the precise contour. The Euclidean distance between the centroid of the target search window in the CamShift algorithm and the new centroid could be as the basis for tracking. If the Euclidean distance within the set threshold, the CamShift algorithm would be used to track the target directly; On the contrary, the new target centroid was regarded as the centroid of the target search window in the current frame to achieve target tracking through the CamShift algorithm. The experimental results showed that the new algorithm had good real-time performance, and the tracking performance was stable and reliable.