Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/581782
Title: Visual Tracking via Random Walks on Graph Model
Authors: Xiaoli Li;Zhifeng Han;Lijun Wang;Huchuan Lu
subject: visual tracking|ergodic Markov chain|Absorbing Markov chain|random walks
Year: 2016
Publisher: IEEE
Abstract: In this paper, we formulate visual tracking as random walks on graph models with nodes representing superpixels and edges denoting relationships between superpixels. We integrate two novel graphs with the theory of Markov random walks, resulting in two Markov chains. First, an ergodic Markov chain is enforced to globally search for the candidate nodes with similar features to the template nodes. Second, an absorbing Markov chain is utilized to model the temporal coherence between consecutive frames. The final confidence map is generated by a structural model which combines both appearance similarity measurement derived by the random walks and internal spatial layout demonstrated by different target parts. The effectiveness of the proposed Markov chains as well as the structural model is evaluated both qualitatively and quantitatively. Experimental results on challenging sequences show that the proposed tracking algorithm performs favorably against state-of-the-art methods.
URI: http://localhost/handle/Hannan/182258
http://localhost/handle/Hannan/581782
ISSN: 2168-2267
2168-2275
volume: 46
issue: 9
Appears in Collections:2016

Files in This Item:
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7210186.pdf2.36 MBAdobe PDFThumbnail
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Title: Visual Tracking via Random Walks on Graph Model
Authors: Xiaoli Li;Zhifeng Han;Lijun Wang;Huchuan Lu
subject: visual tracking|ergodic Markov chain|Absorbing Markov chain|random walks
Year: 2016
Publisher: IEEE
Abstract: In this paper, we formulate visual tracking as random walks on graph models with nodes representing superpixels and edges denoting relationships between superpixels. We integrate two novel graphs with the theory of Markov random walks, resulting in two Markov chains. First, an ergodic Markov chain is enforced to globally search for the candidate nodes with similar features to the template nodes. Second, an absorbing Markov chain is utilized to model the temporal coherence between consecutive frames. The final confidence map is generated by a structural model which combines both appearance similarity measurement derived by the random walks and internal spatial layout demonstrated by different target parts. The effectiveness of the proposed Markov chains as well as the structural model is evaluated both qualitatively and quantitatively. Experimental results on challenging sequences show that the proposed tracking algorithm performs favorably against state-of-the-art methods.
URI: http://localhost/handle/Hannan/182258
http://localhost/handle/Hannan/581782
ISSN: 2168-2267
2168-2275
volume: 46
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7210186.pdf2.36 MBAdobe PDFThumbnail
Preview File
Title: Visual Tracking via Random Walks on Graph Model
Authors: Xiaoli Li;Zhifeng Han;Lijun Wang;Huchuan Lu
subject: visual tracking|ergodic Markov chain|Absorbing Markov chain|random walks
Year: 2016
Publisher: IEEE
Abstract: In this paper, we formulate visual tracking as random walks on graph models with nodes representing superpixels and edges denoting relationships between superpixels. We integrate two novel graphs with the theory of Markov random walks, resulting in two Markov chains. First, an ergodic Markov chain is enforced to globally search for the candidate nodes with similar features to the template nodes. Second, an absorbing Markov chain is utilized to model the temporal coherence between consecutive frames. The final confidence map is generated by a structural model which combines both appearance similarity measurement derived by the random walks and internal spatial layout demonstrated by different target parts. The effectiveness of the proposed Markov chains as well as the structural model is evaluated both qualitatively and quantitatively. Experimental results on challenging sequences show that the proposed tracking algorithm performs favorably against state-of-the-art methods.
URI: http://localhost/handle/Hannan/182258
http://localhost/handle/Hannan/581782
ISSN: 2168-2267
2168-2275
volume: 46
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7210186.pdf2.36 MBAdobe PDFThumbnail
Preview File