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dc.contributor.authorYang, Ming-hsuan-
dc.date.accessioned2017-02-22T01:12:07Z-
dc.date.available2017-02-22T01:12:07Z-
dc.date.issued2015-06-
dc.identifier.citationComputer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on 2015 June, Page. 5388-5396en_US
dc.identifier.isbn978-1-4673-6964-0-
dc.identifier.issn1063-6919-
dc.identifier.issn1063-6919-
dc.identifier.urihttp://ieeexplore.ieee.org/document/7299177/?isnumber=7298593&arnumber=7299177-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/25602-
dc.description.abstractIn this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-view. In this setting, we decompose the task of tracking into translation and scale estimation of objects. We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn discriminative correlation filters from the most confident frames to estimate the scale change. In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.en_US
dc.description.sponsorshipC. Ma is sponsored by China Scholarship Council. X. Yang and C. Zhang are supported in part by the NSFC Grants #61025005, #61129001 and #61221001, STCSM Grants #14XD1402100 and #13511504501, 111 Program Grant #B07022, and the CNKT R&D Program Grant #2012BAH07B01. M.-H. Yang is supported in part by the NSF CAREER Grant #1149783 and NSF IIS Grant #1152576.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectTarget trackingen_US
dc.subjectCorrelationen_US
dc.subjectContexten_US
dc.subjectDetectorsen_US
dc.subjectContext modelingen_US
dc.subjectEstimationen_US
dc.titleLong-Term Correlation Trackingen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/CVPR.2015.7299177-
dc.relation.page5388-5396-
dc.contributor.googleauthorMa, Chao-
dc.contributor.googleauthorYang, Xiaokang-
dc.contributor.googleauthorZhang, Chongyang-
dc.contributor.googleauthorYang, Ming-Hsuan-
dc.relation.code20150012-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF COMPUTER SCIENCE-
dc.identifier.pidmhyang-
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COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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