Visual Tracking Based on Discriminative Compressed Features

Abstract

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