Exploring Gait Recognition in Wild Nighttime Scenes

Currently, gait recognition research is gradually expanding from ideal indoor environments to real-world outdoor scenarios. However, recognition scenarios in practical applications are often more complex than those considered in existing studies. For instance, real-world scenarios present multiple i...

ver descrição completa

Detalhes bibliográficos
Autores: Li, Haotian, Gong, Wenjuan|||0000-0001-7805-3629, Li, Yutong, Wu, Yikai, Li, Kechen, Gonzàlez, Jordi|||0000-0001-8033-0306
Formato: artículo
Fecha de publicación:2025
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:dnet:uabarcelona_::7a3c0272943bcdeea35f08b1d28472fb
Acesso em linha:https://ddd.uab.cat/record/307911
https://dx.doi.org/urn:doi:10.3390/app15010350
Access Level:acceso abierto
Palavra-chave:Gait recognition
Gait recognition dataset
Graph convolution networks
Temporal
Convolutional network
Deep learning
id ES_55e66ca1a5ea9451e77cfce555fe8089
oai_identifier_str oai:dnet:uabarcelona_::7a3c0272943bcdeea35f08b1d28472fb
network_acronym_str ES
network_name_str España
repository_id_str
spelling Exploring Gait Recognition in Wild Nighttime ScenesLi, HaotianGong, Wenjuan|||0000-0001-7805-3629Li, YutongWu, YikaiLi, KechenGonzàlez, Jordi|||0000-0001-8033-0306Gait recognitionGait recognition datasetGraph convolution networksTemporalConvolutional networkDeep learningCurrently, gait recognition research is gradually expanding from ideal indoor environments to real-world outdoor scenarios. However, recognition scenarios in practical applications are often more complex than those considered in existing studies. For instance, real-world scenarios present multiple influencing factors, such as viewpoint variations and diverse carried items. Notably, many gait recognition tasks occur under low-light conditions at night. At present, research on gait recognition in nocturnal environments is relatively limited, and effective methods for nighttime gait recognition are lacking. To address this gap, this study extends gait recognition research to outdoor nighttime environments and introduces the first wild gait dataset encompassing both daytime and nighttime data, named Gait Recognition of Day and Night (GaitDN). Furthermore, to tackle the challenges posed by low-light conditions and other influencing factors in outdoor nighttime gait recognition, we propose a novel pose-based gait recognition framework called GaitSAT. This framework models the intrinsic correlations of human joints by integrating self-attention and graph convolution modules. We conduct a comprehensive evaluation of the proposed method and existing approaches using both the GaitDN dataset and other available datasets. The proposed GaitSAT achieves state-of-the-art performance on the OUMVLP, GREW, Gait3D, and GaitDN datasets, with Rank-1 accuracies of 60.77%, 57.37%, 22.90%, and 86.24%, respectively. Experimental results demonstrate that GaitSAT achieves higher accuracy and superior generalization capabilities compared to state-of-the-art pose-based methods. 22025-01-0120252025-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/307911https://dx.doi.org/urn:doi:10.3390/app15010350reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2020-120611RB-I00open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:uabarcelona_::7a3c0272943bcdeea35f08b1d28472fb2026-06-06T12:50:31Z
dc.title.none.fl_str_mv Exploring Gait Recognition in Wild Nighttime Scenes
title Exploring Gait Recognition in Wild Nighttime Scenes
spellingShingle Exploring Gait Recognition in Wild Nighttime Scenes
Li, Haotian
Gait recognition
Gait recognition dataset
Graph convolution networks
Temporal
Convolutional network
Deep learning
title_short Exploring Gait Recognition in Wild Nighttime Scenes
title_full Exploring Gait Recognition in Wild Nighttime Scenes
title_fullStr Exploring Gait Recognition in Wild Nighttime Scenes
title_full_unstemmed Exploring Gait Recognition in Wild Nighttime Scenes
title_sort Exploring Gait Recognition in Wild Nighttime Scenes
dc.creator.none.fl_str_mv Li, Haotian
Gong, Wenjuan|||0000-0001-7805-3629
Li, Yutong
Wu, Yikai
Li, Kechen
Gonzàlez, Jordi|||0000-0001-8033-0306
author Li, Haotian
author_facet Li, Haotian
Gong, Wenjuan|||0000-0001-7805-3629
Li, Yutong
Wu, Yikai
Li, Kechen
Gonzàlez, Jordi|||0000-0001-8033-0306
author_role author
author2 Gong, Wenjuan|||0000-0001-7805-3629
Li, Yutong
Wu, Yikai
Li, Kechen
Gonzàlez, Jordi|||0000-0001-8033-0306
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Gait recognition
Gait recognition dataset
Graph convolution networks
Temporal
Convolutional network
Deep learning
topic Gait recognition
Gait recognition dataset
Graph convolution networks
Temporal
Convolutional network
Deep learning
description Currently, gait recognition research is gradually expanding from ideal indoor environments to real-world outdoor scenarios. However, recognition scenarios in practical applications are often more complex than those considered in existing studies. For instance, real-world scenarios present multiple influencing factors, such as viewpoint variations and diverse carried items. Notably, many gait recognition tasks occur under low-light conditions at night. At present, research on gait recognition in nocturnal environments is relatively limited, and effective methods for nighttime gait recognition are lacking. To address this gap, this study extends gait recognition research to outdoor nighttime environments and introduces the first wild gait dataset encompassing both daytime and nighttime data, named Gait Recognition of Day and Night (GaitDN). Furthermore, to tackle the challenges posed by low-light conditions and other influencing factors in outdoor nighttime gait recognition, we propose a novel pose-based gait recognition framework called GaitSAT. This framework models the intrinsic correlations of human joints by integrating self-attention and graph convolution modules. We conduct a comprehensive evaluation of the proposed method and existing approaches using both the GaitDN dataset and other available datasets. The proposed GaitSAT achieves state-of-the-art performance on the OUMVLP, GREW, Gait3D, and GaitDN datasets, with Rank-1 accuracies of 60.77%, 57.37%, 22.90%, and 86.24%, respectively. Experimental results demonstrate that GaitSAT achieves higher accuracy and superior generalization capabilities compared to state-of-the-art pose-based methods.
publishDate 2025
dc.date.none.fl_str_mv 2
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/307911
https://dx.doi.org/urn:doi:10.3390/app15010350
url https://ddd.uab.cat/record/307911
https://dx.doi.org/urn:doi:10.3390/app15010350
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2020-120611RB-I00
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869408340939898880
score 15.812429

Registos relacionados