Clothing Status Awareness for Long-Term Person Re-Identification

Huang, Y; Wu, Q; Xu, J; Zhong, Y; Zhang, Z

Clothing Status Awareness for Long-Term Person Re-Identification

Huang, Y Wu, Q

Xu, J Zhong, Y Zhang, Z

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2022, 00, pp. 11875-11884
Issue Date:: 2022-10-18

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Field	Value	Language
dc.contributor.author	Huang, Y
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.contributor.author	Xu, J
dc.contributor.author	Zhong, Y
dc.contributor.author	Zhang, Z
dc.date	2021-10-11
dc.date.accessioned	2023-04-06T02:15:05Z
dc.date.available	2023-04-06T02:15:05Z
dc.date.issued	2022-10-18
dc.identifier.citation	2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2022, 00, pp. 11875-11884
dc.identifier.isbn	978-1-6654-2812-5
dc.identifier.issn	1550-5499
dc.identifier.issn	2380-7504
dc.identifier.uri	http://hdl.handle.net/10453/169271
dc.description.abstract	Long-Term person re-identification (LT-reID) exposes extreme challenges because of the longer time gaps between two recording footages where a person is likely to change clothing. There are two types of approaches for LT-reID: biometrics-based approach and data adaptation based approach. The former one is to seek clothing irrelevant biometric features. However, seeking high quality biometric feature is the main concern. The latter one adopts fine-tuning strategy by using data with significant clothing change. However, the performance is compromised when it is applied to cases without clothing change. This work argues that these approaches in fact are not aware of clothing status (i.e., change or no-change) of a pedestrian. Instead, they blindly assume all footages of a pedestrian have different clothes. To tackle this issue, a Regularization via Clothing Status Awareness Network (RCSANet) is proposed to regularize descriptions of a pedestrian by embedding the clothing status awareness. Consequently, the description can be enhanced to maintain the best ID discriminative feature while improving its robustness to real-world LT-reID where both clothing-change case and no-clothing-change case exist. Experiments show that RCSANet performs reasonably well on three LT-reID datasets.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE/CVF International Conference on Computer Vision (ICCV)
dc.relation.ispartof	IEEE/CVF International Conference on Computer Vision
dc.relation.isbasedon	10.1109/ICCV48922.2021.01168
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Clothing Status Awareness for Long-Term Person Re-Identification
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Montreal
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	*
pubs.consider-herdc	false
dc.date.updated	2023-04-06T02:15:04Z
pubs.finish-date	2021-10-17
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-10-11
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Long-Term person re-identification (LT-reID) exposes extreme challenges because of the longer time gaps between two recording footages where a person is likely to change clothing. There are two types of approaches for LT-reID: biometrics-based approach and data adaptation based approach. The former one is to seek clothing irrelevant biometric features. However, seeking high quality biometric feature is the main concern. The latter one adopts fine-tuning strategy by using data with significant clothing change. However, the performance is compromised when it is applied to cases without clothing change. This work argues that these approaches in fact are not aware of clothing status (i.e., change or no-change) of a pedestrian. Instead, they blindly assume all footages of a pedestrian have different clothes. To tackle this issue, a Regularization via Clothing Status Awareness Network (RCSANet) is proposed to regularize descriptions of a pedestrian by embedding the clothing status awareness. Consequently, the description can be enhanced to maintain the best ID discriminative feature while improving its robustness to real-world LT-reID where both clothing-change case and no-clothing-change case exist. Experiments show that RCSANet performs reasonably well on three LT-reID datasets.

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http://hdl.handle.net/10453/169271