Person Reidentification Based on Adaptive Relation Attention Network in Intelligent Monitoring System for the IoB

Zhang, T; Zhang, S; Jia, W

Person Reidentification Based on Adaptive Relation Attention Network in Intelligent Monitoring System for the IoB

Zhang, T Zhang, S Jia, W

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Engineering Management, 2022, PP, (99), pp. 1-10
Issue Date:: 2022-05-27

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Field	Value	Language
dc.contributor.author	Zhang, T
dc.contributor.author	Zhang, S
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338
dc.date.accessioned	2023-05-16T04:28:05Z
dc.date.available	2023-05-16T04:28:05Z
dc.date.issued	2022-05-27
dc.identifier.citation	IEEE Transactions on Engineering Management, 2022, PP, (99), pp. 1-10
dc.identifier.issn	0018-9391
dc.identifier.issn	1558-0040
dc.identifier.uri	http://hdl.handle.net/10453/170352
dc.description.abstract	The Internet of Behavior (IoB), which combines Internet of Things and artificial intelligence, plays an important role in building smart city. As a significant part of data collection and analysis, intelligent monitoring system needs robust algorithms for analyzing data to make corresponding feedback. The development of person reidentification (re-id) has benefited from deep learning methods, especially for the IoB application. However, most existing person re-id methods under intelligent monitoring cannot solve some problems in the real world, such as occlusion and background cluster. In this article, we propose an adaptive relation attention (ARA) network for person re-id based on the ARA mechanism, which can address the abovementioned challenges effectively. The ARA module consists of relation branch and adaption branch. Relation branch captures the global structural information by mining relation among feature nodes. Adaption branch generates dynamic weights for attention features that relation branch produced. Our constructed intelligent IoB system can acquire the behavior status of pedestrian at different times and places, providing corresponding feedback rapidly. Data transmission and storage in our system are built in a decentralized way, which is based on blockchain. Our person re-id method outperforms many state-of-the-art methods on the CUHK03, Market-1501, and DukeMTMC-reID, showing excellent robustness.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Engineering Management
dc.relation.isbasedon	10.1109/tem.2022.3170550
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Business & Management
dc.title	Person Reidentification Based on Adaptive Relation Attention Network in Intelligent Monitoring System for the IoB
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	15 Commerce, Management, Tourism and Services
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 - 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	open_access	*
utslib.copyright.embargo	2024-05-27T00:00:00+1000Z
dc.date.updated	2023-05-16T04:27:58Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

The Internet of Behavior (IoB), which combines Internet of Things and artificial intelligence, plays an important role in building smart city. As a significant part of data collection and analysis, intelligent monitoring system needs robust algorithms for analyzing data to make corresponding feedback. The development of person reidentification (re-id) has benefited from deep learning methods, especially for the IoB application. However, most existing person re-id methods under intelligent monitoring cannot solve some problems in the real world, such as occlusion and background cluster. In this article, we propose an adaptive relation attention (ARA) network for person re-id based on the ARA mechanism, which can address the abovementioned challenges effectively. The ARA module consists of relation branch and adaption branch. Relation branch captures the global structural information by mining relation among feature nodes. Adaption branch generates dynamic weights for attention features that relation branch produced. Our constructed intelligent IoB system can acquire the behavior status of pedestrian at different times and places, providing corresponding feedback rapidly. Data transmission and storage in our system are built in a decentralized way, which is based on blockchain. Our person re-id method outperforms many state-of-the-art methods on the CUHK03, Market-1501, and DukeMTMC-reID, showing excellent robustness.

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http://hdl.handle.net/10453/170352