Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/617287
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dc.contributor.authorDong Wangen_US
dc.contributor.authorHuchuan Luen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.date.accessioned2020-05-20T09:17:55Z-
dc.date.available2020-05-20T09:17:55Z-
dc.date.issued2016en_US
dc.identifier.issn1051-8215en_US
dc.identifier.issn1558-2205en_US
dc.identifier.other10.1109/TCSVT.2015.2462012en_US
dc.identifier.urihttp://localhost/handle/Hannan/155295en_US
dc.identifier.urihttp://localhost/handle/Hannan/617287-
dc.description.abstractIn this paper, we propose an online tracking algorithm based on a novel robust linear regression estimator. In contrast to existing methods, the proposed least soft-threshold squares (LSS) algorithm models the error term with the Gaussian-Laplacian distribution, which can be efficiently solved. For visual tracking, the Gaussian-Laplacian noise assumption enables our LSS model to handle the normal appearance change and outlier simultaneously. Based on the maximum joint likelihood of parameters, we derive an LSS distance metric to measure the difference between an observation sample and a dictionary of positive templates. Compared with the distance derived from ordinary least squares methods, the proposed metric is more effective in dealing with the outliers. In addition, we provide insights on the relationships among the LSS problem, Huber loss function, and trivial templates, which facilitate better understandings of the existing tracking methods. Finally, we develop a robust tracking algorithm based on the LSS distance metric with an update scheme and negative templates, and speed it up with a particle selection mechanism. Experimental results on numerous challenging image sequences demonstrate that the proposed tracking algorithm performs favorably than the state-of-the-art methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7172503.pdfen_US
dc.subjectlinear representation|Gaussian–Laplacian noise|least soft-threshold squares (LSS)|visual trackingen_US
dc.titleRobust Visual Tracking via Least Soft-Threshold Squaresen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue9en_US
dc.journal.titleIEEE Transactions on Circuits and Systems for Video Technologyen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorDong Wangen_US
dc.contributor.authorHuchuan Luen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.date.accessioned2020-05-20T09:17:55Z-
dc.date.available2020-05-20T09:17:55Z-
dc.date.issued2016en_US
dc.identifier.issn1051-8215en_US
dc.identifier.issn1558-2205en_US
dc.identifier.other10.1109/TCSVT.2015.2462012en_US
dc.identifier.urihttp://localhost/handle/Hannan/155295en_US
dc.identifier.urihttp://localhost/handle/Hannan/617287-
dc.description.abstractIn this paper, we propose an online tracking algorithm based on a novel robust linear regression estimator. In contrast to existing methods, the proposed least soft-threshold squares (LSS) algorithm models the error term with the Gaussian-Laplacian distribution, which can be efficiently solved. For visual tracking, the Gaussian-Laplacian noise assumption enables our LSS model to handle the normal appearance change and outlier simultaneously. Based on the maximum joint likelihood of parameters, we derive an LSS distance metric to measure the difference between an observation sample and a dictionary of positive templates. Compared with the distance derived from ordinary least squares methods, the proposed metric is more effective in dealing with the outliers. In addition, we provide insights on the relationships among the LSS problem, Huber loss function, and trivial templates, which facilitate better understandings of the existing tracking methods. Finally, we develop a robust tracking algorithm based on the LSS distance metric with an update scheme and negative templates, and speed it up with a particle selection mechanism. Experimental results on numerous challenging image sequences demonstrate that the proposed tracking algorithm performs favorably than the state-of-the-art methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7172503.pdfen_US
dc.subjectlinear representation|Gaussian–Laplacian noise|least soft-threshold squares (LSS)|visual trackingen_US
dc.titleRobust Visual Tracking via Least Soft-Threshold Squaresen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue9en_US
dc.journal.titleIEEE Transactions on Circuits and Systems for Video Technologyen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7172503.pdf2.48 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDong Wangen_US
dc.contributor.authorHuchuan Luen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.date.accessioned2020-05-20T09:17:55Z-
dc.date.available2020-05-20T09:17:55Z-
dc.date.issued2016en_US
dc.identifier.issn1051-8215en_US
dc.identifier.issn1558-2205en_US
dc.identifier.other10.1109/TCSVT.2015.2462012en_US
dc.identifier.urihttp://localhost/handle/Hannan/155295en_US
dc.identifier.urihttp://localhost/handle/Hannan/617287-
dc.description.abstractIn this paper, we propose an online tracking algorithm based on a novel robust linear regression estimator. In contrast to existing methods, the proposed least soft-threshold squares (LSS) algorithm models the error term with the Gaussian-Laplacian distribution, which can be efficiently solved. For visual tracking, the Gaussian-Laplacian noise assumption enables our LSS model to handle the normal appearance change and outlier simultaneously. Based on the maximum joint likelihood of parameters, we derive an LSS distance metric to measure the difference between an observation sample and a dictionary of positive templates. Compared with the distance derived from ordinary least squares methods, the proposed metric is more effective in dealing with the outliers. In addition, we provide insights on the relationships among the LSS problem, Huber loss function, and trivial templates, which facilitate better understandings of the existing tracking methods. Finally, we develop a robust tracking algorithm based on the LSS distance metric with an update scheme and negative templates, and speed it up with a particle selection mechanism. Experimental results on numerous challenging image sequences demonstrate that the proposed tracking algorithm performs favorably than the state-of-the-art methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7172503.pdfen_US
dc.subjectlinear representation|Gaussian–Laplacian noise|least soft-threshold squares (LSS)|visual trackingen_US
dc.titleRobust Visual Tracking via Least Soft-Threshold Squaresen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue9en_US
dc.journal.titleIEEE Transactions on Circuits and Systems for Video Technologyen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7172503.pdf2.48 MBAdobe PDFThumbnail
Preview File