Dual-stream guided-learning via a priori optimization for person re-identification

Wu, J; Huang, Y; Wu, Q; Gao, Z; Zhao, J; Huang, L

Dual-stream guided-learning via a priori optimization for person re-identification

Wu, J Huang, Y Wu, Q

Gao, Z Zhao, J Huang, L

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Multimedia Computing, Communications and Applications, 2021, 17, (4), pp. 1-22
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Wu, J
dc.contributor.author	Huang, Y
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.contributor.author	Gao, Z
dc.contributor.author	Zhao, J
dc.contributor.author	Huang, L
dc.date.accessioned	2022-04-18T19:18:59Z
dc.date.available	2022-04-18T19:18:59Z
dc.date.issued	2021-11-01
dc.identifier.citation	ACM Transactions on Multimedia Computing, Communications and Applications, 2021, 17, (4), pp. 1-22
dc.identifier.issn	1551-6857
dc.identifier.issn	1551-6865
dc.identifier.uri	http://hdl.handle.net/10453/156335
dc.description.abstract	The task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Multimedia Computing, Communications and Applications
dc.relation.isbasedon	10.1145/3447715
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Dual-stream guided-learning via a priori optimization for person re-identification
dc.type	Journal Article
utslib.citation.volume	17
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-18T19:18:55Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	4

Abstract:

The task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods.

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http://hdl.handle.net/10453/156335