Gait Attribute Recognition: A New Benchmark for Learning Richer Attributes From Human Gait Patterns

Song, X; Hou, S; Huang, Y; Cao, C; Liu, X; Huang, Y; Shan, C

Gait Attribute Recognition: A New Benchmark for Learning Richer Attributes From Human Gait Patterns

Song, X Hou, S Huang, Y

Cao, C Liu, X Huang, Y

Shan, C

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2024, 19, pp. 1-14
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Song, X
dc.contributor.author	Hou, S
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-1363-5318
dc.contributor.author	Cao, C
dc.contributor.author	Liu, X
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-1363-5318
dc.contributor.author	Shan, C
dc.date.accessioned	2024-08-07T05:49:32Z
dc.date.available	2024-08-07T05:49:32Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2024, 19, pp. 1-14
dc.identifier.issn	1556-6013
dc.identifier.issn	1556-6021
dc.identifier.uri	http://hdl.handle.net/10453/180357
dc.description.abstract	Compared to gait recognition, Gait Attribute Recognition (GAR) is a seldom-investigated problem. However, since gait attribute recognition can provide richer and finer semantic descriptions, it is an indispensable part of building intelligent gait analysis systems. Nonetheless, the types of attributes considered in the existing datasets are very limited. This paper contributes a new benchmark dataset for gait attribute recognition named Multi-Attribute Gait (MA-Gait). Our MA-Gait contains 95 subjects recorded from 12 camera views, resulting in more than 13000 sequences, with 16 attributes labeled, including six attributes that have never been considered in the literature. Moreover, we propose a Multi-Scale Motion Encoder (MSME) to extract robust motion features, and an Attribute-Guided Feature Selection Module (AGFSM) to adaptively capture the most discriminative attribute features from static appearance features and dynamic motion features for different attributes. Our method achieves the best GAR accuracy on the new dataset. Comprehensive experiments show the effectiveness of the proposed method through both quantitative and qualitative evaluations.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security
dc.relation.isbasedon	10.1109/TIFS.2023.3318934
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Gait Attribute Recognition: A New Benchmark for Learning Richer Attributes From Human Gait Patterns
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:49:29Z
pubs.publication-status	Published
pubs.volume	19

Abstract:

Compared to gait recognition, Gait Attribute Recognition (GAR) is a seldom-investigated problem. However, since gait attribute recognition can provide richer and finer semantic descriptions, it is an indispensable part of building intelligent gait analysis systems. Nonetheless, the types of attributes considered in the existing datasets are very limited. This paper contributes a new benchmark dataset for gait attribute recognition named Multi-Attribute Gait (MA-Gait). Our MA-Gait contains 95 subjects recorded from 12 camera views, resulting in more than 13000 sequences, with 16 attributes labeled, including six attributes that have never been considered in the literature. Moreover, we propose a Multi-Scale Motion Encoder (MSME) to extract robust motion features, and an Attribute-Guided Feature Selection Module (AGFSM) to adaptively capture the most discriminative attribute features from static appearance features and dynamic motion features for different attributes. Our method achieves the best GAR accuracy on the new dataset. Comprehensive experiments show the effectiveness of the proposed method through both quantitative and qualitative evaluations.

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http://hdl.handle.net/10453/180357