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An adaptive padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1845-1860页

作者： Zihang Feng Liping Yan Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems the School of AutomationBeijing Institute of TechnologyBeijing 100081China IEEE the School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China

In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter.However,the related studies are insufficient.By exploring the potential of trackers in these two aspects,a novel adaptive padding correlation filter(APCF)with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework.In the tracker,three feature groups are fused by use of the weighted sum of the normalized response maps,to alleviate the risk of drift caused by the extreme change of single feature.Moreover,to improve the adaptive ability of padding for the filter training of different object shapes,the best padding is selected from the preset pool according to tracking precision over the whole video,where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames.The sequence features include three traditional features and eight newly constructed features.Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.

关键词： adaptive padding context information correlation filter(CF) feature group fusion robust visual tracking

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