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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2779275
|2 doi
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|a eng
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| 100 |
1 |
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|a Yao Sui
|e verfasserin
|4 aut
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| 245 |
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|a Exploiting Spatial-Temporal Locality of Tracking via Structured Dictionary Learning
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|c 2018
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 11.12.2018
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|a Date Revised 11.12.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, a novel spatial-temporal locality is proposed and unified via a discriminative dictionary learning framework for visual tracking. By exploring the strong local correlations between temporally obtained target and their spatially distributed nearby background neighbors, a spatial-temporal locality is obtained. The locality is formulated as a subspace model and exploited under a unified structure of discriminative dictionary learning with a subspace structure. Using the learned dictionary, the target and its background can be described and distinguished effectively through their sparse codes. As a result, the target is localized by integrating both the descriptive and the discriminative qualities. Extensive experiments on various challenging video sequences demonstrate the superior performance of proposed algorithm over the other state-of-the-art approaches
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|a Journal Article
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| 700 |
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|a Guanghui Wang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li Zhang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ming-Hsuan Yang
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 3 vom: 14. März, Seite 1282-1296
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|x 1941-0042
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|g volume:27
|g year:2018
|g number:3
|g day:14
|g month:03
|g pages:1282-1296
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|u http://dx.doi.org/10.1109/TIP.2017.2779275
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