Aiming at the problem that the compression perceptual Hash algorithm based on discrete cosine transform is difficult to track the target in the case of illumination change, target deformation or local occlusion, this paper proposes an enhanced template-matching Hash algorithm based on interclass variance and discrete cosine transform. This algorithm is a target tracking algorithm with automatic template updating, which uses the interclass variance threshold segmentation and discrete cosine transform to extract the different characteristic information of the target, the rapidly intensified method of difference to generate the Hashing sequence for reducing the influence of illumination, and the drawer principle to shorten the time for Hamming distance comparison. Experiments were made by using this algorithm, the traditional Hash algorithm, and the compression perceptual Hash algorithm based on DCT for tracking the three public standard videos of David, Girl, and CarScale.The results show that, the algorithm can improve the success rate of the target tracking under illumination change, target deformation or local occlusion, has good robustness and satisfies the real-time tracking requirements.