Improving person re-identification by attribute and identity learning

•We annotate attribute labels on two large-scale person re-identification datasets.•We propose APR to improve re-ID by exploiting global and detailed information.•We introduce a module to leverage the correlation between attributes.•We speed-up the retrieval of re-ID by ten times with only a 2.92% a...

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Vydané v:	Pattern recognition Ročník 95; s. 151 - 161
Hlavní autori:	Lin, Yutian, Zheng, Liang, Zheng, Zhedong, Wu, Yu, Hu, Zhilan, Yan, Chenggang, Yang, Yi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.11.2019
Predmet:	Attribute recognition Person re-identification Attribute recognition Person re-identification
ISSN:	0031-3203, 1873-5142
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	•We annotate attribute labels on two large-scale person re-identification datasets.•We propose APR to improve re-ID by exploiting global and detailed information.•We introduce a module to leverage the correlation between attributes.•We speed-up the retrieval of re-ID by ten times with only a 2.92% accuracy drop.•We achieve competitive re-ID performance with the state-of-the-art methods. Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID methods only take identity labels of pedestrians into consideration. However, we find the attributes, containing detailed local descriptions, are beneficial in allowing the re-ID model to learn more discriminative feature representations. In this paper, based on the complementarity of attribute labels and ID labels, we propose an attribute-person recognition (APR) network, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes. We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other. In addition, we re-weight the attribute predictions considering the dependencies and correlations among the attributes. The experimental results on two large-scale re-ID benchmarks demonstrate that by learning a more discriminative representation, APR achieves competitive re-ID performance compared with the state-of-the-art methods. We use APR to speed up the retrieval process by ten times with a minor accuracy drop of 2.92% on Market-1501. Besides, we also apply APR on the attribute recognition task and demonstrate improvement over the baselines.
AbstractList	•We annotate attribute labels on two large-scale person re-identification datasets.•We propose APR to improve re-ID by exploiting global and detailed information.•We introduce a module to leverage the correlation between attributes.•We speed-up the retrieval of re-ID by ten times with only a 2.92% accuracy drop.•We achieve competitive re-ID performance with the state-of-the-art methods. Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID methods only take identity labels of pedestrians into consideration. However, we find the attributes, containing detailed local descriptions, are beneficial in allowing the re-ID model to learn more discriminative feature representations. In this paper, based on the complementarity of attribute labels and ID labels, we propose an attribute-person recognition (APR) network, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes. We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other. In addition, we re-weight the attribute predictions considering the dependencies and correlations among the attributes. The experimental results on two large-scale re-ID benchmarks demonstrate that by learning a more discriminative representation, APR achieves competitive re-ID performance compared with the state-of-the-art methods. We use APR to speed up the retrieval process by ten times with a minor accuracy drop of 2.92% on Market-1501. Besides, we also apply APR on the attribute recognition task and demonstrate improvement over the baselines.
Author	Wu, Yu Hu, Zhilan Yan, Chenggang Zheng, Zhedong Zheng, Liang Lin, Yutian Yang, Yi
Author_xml	– sequence: 1 givenname: Yutian surname: Lin fullname: Lin, Yutian organization: Center for Artificial Intelligence, University of Technology Sydney, Australia – sequence: 2 givenname: Liang orcidid: 0000-0002-1109-3893 surname: Zheng fullname: Zheng, Liang organization: Australian National University, Australia – sequence: 3 givenname: Zhedong surname: Zheng fullname: Zheng, Zhedong organization: Center for Artificial Intelligence, University of Technology Sydney, Australia – sequence: 4 givenname: Yu surname: Wu fullname: Wu, Yu organization: Center for Artificial Intelligence, University of Technology Sydney, Australia – sequence: 5 givenname: Zhilan surname: Hu fullname: Hu, Zhilan organization: Center for Artificial Intelligence, University of Technology Sydney, Australia – sequence: 6 givenname: Chenggang surname: Yan fullname: Yan, Chenggang organization: Hangzhou Dianzi University, China – sequence: 7 givenname: Yi surname: Yang fullname: Yang, Yi email: yi.yang@uts.edu.au organization: Center for Artificial Intelligence, University of Technology Sydney, Australia
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Snippet	•We annotate attribute labels on two large-scale person re-identification datasets.•We propose APR to improve re-ID by exploiting global and detailed...
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SubjectTerms	Attribute recognition Person re-identification
Title	Improving person re-identification by attribute and identity learning
URI	https://dx.doi.org/10.1016/j.patcog.2019.06.006
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