Human action recognition using 2D poses

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorScience and Technology of São Paulo-
Autor(es): dc.creatorVarges Da Silva, Murilo-
Autor(es): dc.creatorNilceu Marana, Aparecido [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:24:12Z-
Data de disponibilização: dc.date.available2022-02-22T00:24:12Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/BRACIS.2019.00134-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/198320-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/198320-
Descrição: dc.descriptionThe advances in video capture, storage and sharing technologies have caused a high demand in techniques for automatic recognition of humans actions. Among the main applications, we can highlight surveillance in public places, detection of falls in the elderly, no-checkout-required stores (Amazon Go), self-driving car, inappropriate content posted on the Internet, etc. The automatic recognition of human actions in videos is a challenging task because in order to obtain a good result one has to work with spatial information (e.g., shapes found in a single frame) and temporal information (e.g., movements found across frames). In this work, we present a simple methodology for describing human actions in videos that use extracted data from 2-Dimensional poses. The experimental results show that the proposed technique can encode spatial and temporal information, obtaining competitive accuracy rates compared to state-of-the-art methods.-
Descrição: dc.descriptionFederal University of São Carlos - UFSCar-
Descrição: dc.descriptionFaculty of Sciences - UNESP-
Descrição: dc.descriptionFederal Institute of Education Science and Technology of São Paulo-
Descrição: dc.descriptionFaculty of Sciences - UNESP-
Formato: dc.format747-752-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectHuman action recognition-
Palavras-chave: dc.subjectSpatio-temporal features-
Palavras-chave: dc.subjectSurveillance systems-
Palavras-chave: dc.subjectVideo sequences-
Título: dc.titleHuman action recognition using 2D poses-
Aparece nas coleções:Repositório Institucional - Unesp

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