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GRIDDS - a gait recognition image and depth dataset

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Resumo:Several approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes.
Autores principais:Nunes, João
Outros Autores:Moreira, Pedro Miguel; Tavares, João Manuel R. S.
Assunto:Gait Dataset Person Recognition Gender Recognition RGB-D Sensors GRIDDS
Ano:2019
País:Portugal
Tipo de documento:capítulo de livro
Tipo de acesso:acesso a metadados
Instituição associada:Instituto Politécnico de Viana do Castelo
Idioma:inglês
Origem:Repositório Científico IPVC
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author Nunes, João
author2 Moreira, Pedro Miguel
Tavares, João Manuel R. S.
author2_role author
author
author_facet Nunes, João
Moreira, Pedro Miguel
Tavares, João Manuel R. S.
author_role author
country_str PT
creators_json_txt [{\"Person.name\":\"Nunes, João\"},{\"Person.name\":\"Moreira, Pedro Miguel\"},{\"Person.name\":\"Tavares, João Manuel R. S.\"}]
datacite.creators.creator.creatorName.fl_str_mv Nunes, João
Moreira, Pedro Miguel
Tavares, João Manuel R. S.
datacite.date.Accepted.fl_str_mv 2019-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2023-01-06T18:40:24Z
datacite.date.embargoed.fl_str_mv 2023-01-06T18:40:24Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_14cb
datacite.subjects.subject.fl_str_mv Gait Dataset
Person Recognition
Gender Recognition
RGB-D Sensors
GRIDDS
datacite.titles.title.fl_str_mv GRIDDS - a gait recognition image and depth dataset
dc.creator.none.fl_str_mv Nunes, João
Moreira, Pedro Miguel
Tavares, João Manuel R. S.
dc.date.Accepted.fl_str_mv 2019-01-01T00:00:00Z
dc.date.available.fl_str_mv 2023-01-06T18:40:24Z
dc.date.embargoed.fl_str_mv 2023-01-06T18:40:24Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11960/3099
dc.language.none.fl_str_mv eng
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_14cb
dc.subject.none.fl_str_mv Gait Dataset
Person Recognition
Gender Recognition
RGB-D Sensors
GRIDDS
dc.title.fl_str_mv GRIDDS - a gait recognition image and depth dataset
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_3248
description Several approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes.
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id ripvc_a91dc8d75dfd06df09666f97c6bb3085
identifier.url.fl_str_mv http://hdl.handle.net/20.500.11960/3099
instacron_str ipvc
institution Instituto Politécnico de Viana do Castelo
instname_str Instituto Politécnico de Viana do Castelo
language eng
network_acronym_str ripvc
network_name_str Repositório Científico IPVC
oai_identifier_str oai:repositorio.ipvc.pt:20.500.11960/3099
organization_str_mv urn:organizationAcronym:ipvc
person_str_mv Nunes, João
Moreira, Pedro Miguel
Tavares, João Manuel R. S.
publishDate 2019
reponame_str Repositório Científico IPVC
repository_id_str urn:repositoryAcronym:ripvc
service_str_mv urn:repositoryAcronym:ripvc
spelling pt_PTSeveral approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes.application/pdfengpt_PTGRIDDS - a gait recognition image and depth datasetNunes, JoãoMoreira, Pedro MiguelTavares, João Manuel R. S.Handlehttp://hdl.handle.net/20.500.11960/3099ISBNIsPartOf978-3-030-32039-3ISBNIsPartOf978-3-030-32040-9ISSNIsPartOf2212-9413ISSNIsPartOf2212-9391DOIIsPartOf10.1007/978-3-030-32040-9_362023-01-06T18:40:24Z2019-01-01T00:00:00Z20192022-11-02T16:56:26Zhttp://purl.org/coar/access_right/c_14cbmetadata only accesspt_PTGait Datasetpt_PTPerson Recognitionpt_PTGender Recognitionpt_PTRGB-D Sensorspt_PTGRIDDS807606 byteshttp://purl.org/coar/access_right/c_14cbapplication/pdffulltexthttp://repositorio.ipvc.pt/bitstream/20.500.11960/3099/1/370707.pdfliteraturehttp://purl.org/coar/resource_type/c_3248book part
spellingShingle GRIDDS - a gait recognition image and depth dataset
Nunes, João
Gait Dataset
Person Recognition
Gender Recognition
RGB-D Sensors
GRIDDS
status SINGLETON
subject.fl_str_mv Gait Dataset
Person Recognition
Gender Recognition
RGB-D Sensors
GRIDDS
title GRIDDS - a gait recognition image and depth dataset
title_full GRIDDS - a gait recognition image and depth dataset
title_fullStr GRIDDS - a gait recognition image and depth dataset
title_full_unstemmed GRIDDS - a gait recognition image and depth dataset
title_short GRIDDS - a gait recognition image and depth dataset
title_sort GRIDDS - a gait recognition image and depth dataset
topic Gait Dataset
Person Recognition
Gender Recognition
RGB-D Sensors
GRIDDS
topic_facet Gait Dataset
Person Recognition
Gender Recognition
RGB-D Sensors
GRIDDS
url http://hdl.handle.net/20.500.11960/3099
visible 1