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Advances in Multimedia
Volume 2018, Article ID 7481645, 6 pages
Research Article

Visual Tracking Based on Discriminative Compressed Features

1Department of Modern Education Technology, Ludong University, Yantai, China
2Lab, CNCERT/CC, Yumin Road No. 3A, Beijing 100029, China

Correspondence should be addressed to Wei Liu; moc.anis@wludl

Received 3 April 2018; Revised 13 June 2018; Accepted 11 July 2018; Published 1 August 2018

Academic Editor: Lei Zhang

Copyright © 2018 Wei Liu and Hui Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.