Correlation Filter Tracker With Sample-Reliability Awareness and Self-Guided Update

作     者：Zhou, Lifang Li, Jiaqi Lei, Bangjun Li, Weisheng Leng, Jiaxu 

作者机构：Chongqing Univ Posts & Telecommun Key Lab Image Cognit Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Software Chongqing 400065 Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Peoples R China Chongqing Inst Brain & Intelligence Guangyang Bay Lab Chongqing 400064 Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY》 (IEEE Trans Circuits Syst Video Technol)

年 卷 期：2023年第33卷第1期

页      面：118-131页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Science and Technology Research Program of Chongqing Municipal Education Commission [KJZD-K201900601, KJQN-202100627] Chongqing Excellent Scientist Project [cstc2021ycjh-bgzxm0339] Natural Science Foundation of Chongqing [cstc2019jcyj-msxmX0461] Special Project on Technological Innovation and Application Development [cstc2020jscx-dxwtB0032] National Natural Science Foundation of China [62102057, 62036007, 62050175] Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering [2020SDSJ01] Construction fund for Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering [2019ZYYD007] Graduate Scientific Research and Innovation Foundation of Chongqing [CYS20267] State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering/Key Laboratory of Ministry of Education, Guizhou University [KY-2022(376] 

主　　题：Target tracking Reliability Training Tracking Correlation Visualization Optimization Visual object tracking correlation filter model updating sample reliability 

摘      要：In visual tracking, unreliable samples always exist because of occlusion, illumination variation, motion blur, etc. Existing studies have effectively improved the performance of trackers by enhancing the quality of online samples. However, an underappreciated view is that not all samples are equally essential to model training. In this paper, we propose a Sample-Aware Adaptive Updating (SAAU) strategy which can actively adjust the update formula by sensing the reliability of samples. Specifically, the Sample-Reliability Awareness (SRA) module can quantify sample reliability by calculating three specific indicators, where the Residual Peak-to-Correlation Energy (RPCE) is designed to cooperate with the other two introduced indicators to obtain credit scores on each sample. Besides, the Self-Guided Update (SGU) module provides a tracker with an unfixed learning rate that matches with the reliability label during updating, where our label annotator generates the label. Extensive experiments on several public benchmarks demonstrate the outstanding compatibility of SAAU and the superiority of our tracker (SAAU-CF) over state-of-the-art approaches.