Improving person re-identification by attribute and identity learning. (November 2019)

• Record Type: Journal Article
• Authors: Lin, Yutian; Zheng, Liang; Zheng, Zhedong; Wu, Yu; Hu, Zhilan; Yan, Chenggang; Yang, Yi
• Is Part Of: Pattern recognition. Volume 95(2019:Nov.)
• Page Start: 151
• Page End: 161
• Publication Date: 2019-11
• Subjects: Person re-identification -- Attribute recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.06.006
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: 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. Abstract: 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.