Part-based Collaborative Spatio-temporal Feature Learning for Cloth-changing Gait Recognition

Yao, L; Kusakunniran, W; Wu, Q; Zhang, J; Xu, J

Part-based Collaborative Spatio-temporal Feature Learning for Cloth-changing Gait Recognition

Yao, L Kusakunniran, W Wu, Q

Zhang, J

Xu, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2020, 00, pp. 2057-2064
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Yao, L
dc.contributor.author	Kusakunniran, W
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541
dc.contributor.author	Xu, J
dc.date	2021-01-10
dc.date.accessioned	2021-08-19T00:55:15Z
dc.date.available	2021-08-19T00:55:15Z
dc.date.issued	2020-01-01
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2020, 00, pp. 2057-2064
dc.identifier.isbn	9781728188089
dc.identifier.issn	1051-4651
dc.identifier.uri	http://hdl.handle.net/10453/150162
dc.description.abstract	In decades many gait recognition methods have been proposed using different techniques. However, due to a real-world scenario of clothing variations, a reduction of the recognition rate occurs for most of these methods. Thus in this paper, a part-based spatio-temporal feature learning method is proposed to tackle the problem of clothing variations for gait recognition. First, based on the anatomical properties, human bodies are segmented into two regions, which are affected and unaffected by clothing variations. A learning network is particularly proposed in this paper to grasp principal spatio-temporal features from those unaffected regions. Different from most part-based methods with spatial or temporal features solely being utilized, in our method these two features are associated in a more collaborative manner. Snapshots are created for each gait sequence from the H − W and T − W views. Stable spatial information is embedded in the H −W view and adequate temporal information is embedded in the T −W view. An inherent relationship exists between these two views. Thus, a collaborative spatio-temporal feature will be hybridized by concatenating these correlative spatial and temporal information. The robustness and efficiency of our proposed method are validated by experiments on CASIA Gait Dataset B and OU-ISIR Treadmill Gait Dataset B. Our proposed method can both achieve the state-of-the-art results on these two databases.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition
dc.relation.ispartof	2020 25th International Conference on Pattern Recognition (ICPR)
dc.relation.isbasedon	10.1109/ICPR48806.2021.9412714
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Part-based Collaborative Spatio-temporal Feature Learning for Cloth-changing Gait Recognition
dc.type	Conference Proceeding
utslib.citation.volume	00
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	2021-08-19T00:55:14Z
pubs.finish-date	2021-01-15
pubs.publication-status	Published
pubs.start-date	2021-01-10
pubs.volume	00

Abstract:

In decades many gait recognition methods have been proposed using different techniques. However, due to a real-world scenario of clothing variations, a reduction of the recognition rate occurs for most of these methods. Thus in this paper, a part-based spatio-temporal feature learning method is proposed to tackle the problem of clothing variations for gait recognition. First, based on the anatomical properties, human bodies are segmented into two regions, which are affected and unaffected by clothing variations. A learning network is particularly proposed in this paper to grasp principal spatio-temporal features from those unaffected regions. Different from most part-based methods with spatial or temporal features solely being utilized, in our method these two features are associated in a more collaborative manner. Snapshots are created for each gait sequence from the H − W and T − W views. Stable spatial information is embedded in the H −W view and adequate temporal information is embedded in the T −W view. An inherent relationship exists between these two views. Thus, a collaborative spatio-temporal feature will be hybridized by concatenating these correlative spatial and temporal information. The robustness and efficiency of our proposed method are validated by experiments on CASIA Gait Dataset B and OU-ISIR Treadmill Gait Dataset B. Our proposed method can both achieve the state-of-the-art results on these two databases.

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http://hdl.handle.net/10453/150162