Visual tracking is a challenging research topic in the field of computer vision with many potential applications. A large number of\ntracking methods have been proposed and achieved designed tracking performance.However, the current state-of-the-art tracking\nmethods still can not meet the requirements of real-world applications. One of the main challenges is to design a good appearance\nmodel to describe the targetâ??s appearance. In this paper, we propose a novel visual tracking method, which uses compressed features\nto model targetâ??s appearances and then uses SVM to distinguish the target from its background. The compressed features were\nobtained by the zero-tree coding on multi scale wavelet coefficients extracted from an image, which have both the low dimensionality\nand discriminate ability and therefore ensure to achieve better tracking results. The experimental comparisons with several state of-\nthe-art methods demonstrate the superiority of the proposed method.
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