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|a eng
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| 100 |
1 |
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|a Wang, Taiqing
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Person Re-Identification by Discriminative Selection in Video Ranking
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| 264 |
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1 |
|c 2016
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|a Date Completed 16.01.2018
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| 500 |
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|a Date Revised 16.01.2018
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| 500 |
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID 2011, iLIDS-VID, and HDA+ image sequence datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-/multi-shot ReID methods
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|a Journal Article
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| 700 |
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|a Gong, Shaogang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Xiatian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Shengjin
|e verfasserin
|4 aut
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| 773 |
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8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 38(2016), 12 vom: 01. Dez., Seite 2501-2514
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