Robust visual tracking via adaptive feature channel selection

International Journal of Intelligent SystemsVolume 37, Issue 10 p. 6951-6977 RESEARCH ARTICLE Robust visual tracking via adaptive feature channel selection Sugang Ma, Sugang Ma orcid.org/0000-0002-8588-8111 School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China School of Information Engineering, Chang'an University, Xi'an, ChinaSearch for more papers by this authorLei Zhang, Corresponding Author Lei Zhang zl_hnly@163.com orcid.org/0000-0003-1520-9360 School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China Correspondence Lei Zhang, School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121 Shaanxi, China. Email: zl_hnly@163.comSearch for more papers by this authorZhiqiang Hou, Zhiqiang Hou School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, ChinaSearch for more papers by this authorXiaobao Yang, Xiaobao Yang School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an University of Posts and Telecommunications, Xi'an, ChinaSearch for more papers by this authorLei Pu, Lei Pu orcid.org/0000-0002-4397-2155 School of Operational Support, Rocket Force Engineering University, Xi'an, ChinaSearch for more papers by this authorXiangmo Zhao, Xiangmo Zhao School of Information Engineering, Chang'an University, Xi'an, ChinaSearch for more papers by this author Sugang Ma, Sugang Ma orcid.org/0000-0002-8588-8111 School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China School of Information Engineering, Chang'an University, Xi'an, ChinaSearch for more papers by this authorLei Zhang, Corresponding Author Lei Zhang zl_hnly@163.com orcid.org/0000-0003-1520-9360 School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China Correspondence Lei Zhang, School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121 Shaanxi, China. Email: zl_hnly@163.comSearch for more papers by this authorZhiqiang Hou, Zhiqiang Hou School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, ChinaSearch for more papers by this authorXiaobao Yang, Xiaobao Yang School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an University of Posts and Telecommunications, Xi'an, ChinaSearch for more papers by this authorLei Pu, Lei Pu orcid.org/0000-0002-4397-2155 School of Operational Support, Rocket Force Engineering University, Xi'an, ChinaSearch for more papers by this authorXiangmo Zhao, Xiangmo Zhao School of Information Engineering, Chang'an University, Xi'an, ChinaSearch for more papers by this author First published: 07 March 2022 https://doi.org/10.1002/int.22869Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Discriminative correlation filters (DCFs) have shown promising tracking performance in recent years thanks to the powerful representation ability of deep features. However, a large number of target-irrelevant channels in deep features limits the tracking performance and increases the computational cost. To eliminate the negative impact of noisy channels and improve the utilization efficiency of deep features in DCF-based trackers, we present an adaptive feature channel selection method for robust visual tracking. Our method adaptively chooses the most discriminative channels to learn a more robust target appearance model, which is achieved by evaluating the energy relationship between background and foreground in each feature channel. Moreover, according to the feedback of channel selection, an adaptive model update strategy is proposed to alleviate the model degradation problem caused by incorrect model updating. Extensive experimental results obtained on five popular tracking benchmarks demonstrate the effectiveness of the proposed algorithm and its superiority over the state-of-the-art trackers. CONFLICTS OF INTEREST We declare that we do not have any commercial or associative interest that represents a conflicts of interest in connection with the work submitted. Volume37, Issue10October 2022Pages 6951-6977 RelatedInformation