Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/657467
Title: Discriminative Hash Tracking With Group Sparsity
Authors: Dandan Du;Lihe Zhang;Huchuan Lu;Xue Mei;Xiaoli Li
subject: Feature selection|hash functions|object tracking
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a novel tracking framework based on discriminative supervised hashing algorithm. Different from previous methods, we treat tracking as a problem of object matching in a binary space. Using the hash functions, all target templates and candidates are mapped into compact binary codes, with which the target matching is conducted effectively. To be specific, we make full use of the label information to assign a compact and discriminative binary code for each sample. And to deal with out-of-sample case, multiple hash functions are trained to describe the learned binary codes, and group sparsity is introduced to the hash projection matrix to select the representative and discriminative features dynamically, which is crucial for the tracker to adapt to target appearance variations. The whole training problem is formulated as an optimization function where the hash codes and hash function are learned jointly. Extensive experiments on various challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.
URI: http://localhost/handle/Hannan/168840
http://localhost/handle/Hannan/657467
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

Files in This Item:
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7182292.pdf2.68 MBAdobe PDFThumbnail
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Title: Discriminative Hash Tracking With Group Sparsity
Authors: Dandan Du;Lihe Zhang;Huchuan Lu;Xue Mei;Xiaoli Li
subject: Feature selection|hash functions|object tracking
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a novel tracking framework based on discriminative supervised hashing algorithm. Different from previous methods, we treat tracking as a problem of object matching in a binary space. Using the hash functions, all target templates and candidates are mapped into compact binary codes, with which the target matching is conducted effectively. To be specific, we make full use of the label information to assign a compact and discriminative binary code for each sample. And to deal with out-of-sample case, multiple hash functions are trained to describe the learned binary codes, and group sparsity is introduced to the hash projection matrix to select the representative and discriminative features dynamically, which is crucial for the tracker to adapt to target appearance variations. The whole training problem is formulated as an optimization function where the hash codes and hash function are learned jointly. Extensive experiments on various challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.
URI: http://localhost/handle/Hannan/168840
http://localhost/handle/Hannan/657467
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7182292.pdf2.68 MBAdobe PDFThumbnail
Preview File
Title: Discriminative Hash Tracking With Group Sparsity
Authors: Dandan Du;Lihe Zhang;Huchuan Lu;Xue Mei;Xiaoli Li
subject: Feature selection|hash functions|object tracking
Year: 2016
Publisher: IEEE
Abstract: In this paper, we propose a novel tracking framework based on discriminative supervised hashing algorithm. Different from previous methods, we treat tracking as a problem of object matching in a binary space. Using the hash functions, all target templates and candidates are mapped into compact binary codes, with which the target matching is conducted effectively. To be specific, we make full use of the label information to assign a compact and discriminative binary code for each sample. And to deal with out-of-sample case, multiple hash functions are trained to describe the learned binary codes, and group sparsity is introduced to the hash projection matrix to select the representative and discriminative features dynamically, which is crucial for the tracker to adapt to target appearance variations. The whole training problem is formulated as an optimization function where the hash codes and hash function are learned jointly. Extensive experiments on various challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.
URI: http://localhost/handle/Hannan/168840
http://localhost/handle/Hannan/657467
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7182292.pdf2.68 MBAdobe PDFThumbnail
Preview File