Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/617287
Title: Robust Visual Tracking via Least Soft-Threshold Squares
Authors: Dong Wang;Huchuan Lu;Ming-Hsuan Yang
subject: linear representation|Gaussian–Laplacian noise|least soft-threshold squares (LSS)|visual tracking
Year: 2016
Publisher: IEEE
Abstract: In 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.
URI: http://localhost/handle/Hannan/155295
http://localhost/handle/Hannan/617287
ISSN: 1051-8215
1558-2205
volume: 26
issue: 9
Appears in Collections:2016

Files in This Item:
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Title: Robust Visual Tracking via Least Soft-Threshold Squares
Authors: Dong Wang;Huchuan Lu;Ming-Hsuan Yang
subject: linear representation|Gaussian–Laplacian noise|least soft-threshold squares (LSS)|visual tracking
Year: 2016
Publisher: IEEE
Abstract: In 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.
URI: http://localhost/handle/Hannan/155295
http://localhost/handle/Hannan/617287
ISSN: 1051-8215
1558-2205
volume: 26
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7172503.pdf2.48 MBAdobe PDFThumbnail
Preview File
Title: Robust Visual Tracking via Least Soft-Threshold Squares
Authors: Dong Wang;Huchuan Lu;Ming-Hsuan Yang
subject: linear representation|Gaussian–Laplacian noise|least soft-threshold squares (LSS)|visual tracking
Year: 2016
Publisher: IEEE
Abstract: In 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.
URI: http://localhost/handle/Hannan/155295
http://localhost/handle/Hannan/617287
ISSN: 1051-8215
1558-2205
volume: 26
issue: 9
Appears in Collections:2016

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