Human action recognition in videos based on spatiotemporal features and bag-of-poses

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorScience and Technology of São Paulo-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorVarges da Silva, Murilo-
Autor(es): dc.creatorNilceu Marana, Aparecido [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:26:26Z-
Data de disponibilização: dc.date.available2022-02-22T00:26:26Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.asoc.2020.106513-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/199093-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/199093-
Descrição: dc.descriptionCurrently, there is a large number of methods that use 2D poses to represent and recognize human action in videos. Most of these methods use information computed from raw 2D poses based on the straight line segments that form the body parts in a 2D pose model in order to extract features (e.g., angles and trajectories). In our work, we propose a new method of representing 2D poses. Instead of directly using the straight line segments, firstly, the 2D pose is converted to the parameter space in which each segment is mapped to a point. Then, from the parameter space, spatiotemporal features are extracted and encoded using a Bag-of-Poses approach, then used for human action recognition in the video. Experiments on two well-known public datasets, Weizmann and KTH, showed that the proposed method using 2D poses encoded in parameter space can improve the recognition rates, obtaining competitive accuracy rates compared to state-of-the-art methods.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionDepartment of Computing UFSCar - Federal University of São Carlos, Rod. Washington Luís, Km 235-
Descrição: dc.descriptionDepartment of Computing IFSP - Federal Institute of Education Science and Technology of São Paulo, Rua Pedro Cavalo, 709-
Descrição: dc.descriptionDepartment of Computing Faculty of Sciences UNESP - São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-
Descrição: dc.descriptionDepartment of Computing Faculty of Sciences UNESP - São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-
Idioma: dc.languageen-
Relação: dc.relationApplied Soft Computing Journal-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBag-of-poses-
Palavras-chave: dc.subjectHuman action recognition-
Palavras-chave: dc.subjectSpatiotemporal features-
Palavras-chave: dc.subjectSurveillance systems-
Palavras-chave: dc.subjectVideo sequences-
Título: dc.titleHuman action recognition in videos based on spatiotemporal features and bag-of-poses-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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