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An image representation of skeletal data for action recognition using convolutional neural networks

dc.creatorVernikos I., Mathe E., Papadakis A., Spyrou E., Mylonas P.en
dc.date.accessioned2023-01-31T10:32:20Z
dc.date.available2023-01-31T10:32:20Z
dc.date.issued2019
dc.identifier10.1145/3316782.3322740
dc.identifier.isbn9781450362320
dc.identifier.urihttp://hdl.handle.net/11615/80591
dc.description.abstractIn this paper we present preliminary results of an approach for understanding human actions, based on a novel 2D image representation for 3D skeletal data. More specifically, motion information for human skeletal joints is transformed to a pseudo-colored image. A Convolutional Neural Network is then used for classification. Our approach is evaluated for actions that may be used in an ambient assisted living scenario. © 2019 Association for Computing Machinery.en
dc.language.isoenen
dc.sourceACM International Conference Proceeding Seriesen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85069191168&doi=10.1145%2f3316782.3322740&partnerID=40&md5=1b7a4df1f83823b565eee446e16b1b24
dc.subjectConvolutionen
dc.subjectKnowledge representationen
dc.subjectAction recognitionen
dc.subjectAmbient assisted livingen
dc.subjectColored imagesen
dc.subjectHuman actionsen
dc.subjectHuman activity recognitionen
dc.subjectImage representationsen
dc.subjectMotion informationen
dc.subjectSkeletal jointsen
dc.subjectConvolutional neural networksen
dc.subjectAssociation for Computing Machineryen
dc.titleAn image representation of skeletal data for action recognition using convolutional neural networksen
dc.typeconferenceItemen


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