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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Veröffentlicht in:Pattern recognition Jg. 95; S. 151 - 161
Hauptverfasser: Lin, Yutian, Zheng, Liang, Zheng, Zhedong, Wu, Yu, Hu, Zhilan, Yan, Chenggang, Yang, Yi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2019
Schlagworte:
Attribute recognition
Person re-identification
Attribute recognition
Person re-identification
ISSN:0031-3203, 1873-5142
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Zusammenfassung:•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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2019.06.006