Counting Human Actions in Video During Physical Exercise

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dc.contributor.authorOzeroglu, Burak
dc.contributor.authorSaykol, Ediz
dc.date.accessioned2024-03-13T10:30:19Z
dc.date.available2024-03-13T10:30:19Z
dc.date.issued2015
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description22nd International Conference on Neural Information Processing (ICONIP) -- NOV 09-12, 2015 -- Istanbul, TURKEYen_US
dc.description.abstractWe present a simple yet effective human action detection and counting scheme during physical exercise using video stream data. Counting human actions automatically is more meaningful for data analysis in sports centers, and for healthiness observations in rehabilitation centers. The identification of the action starts with the detection of crucial body regions, namely skeletal joints. We observed that hand-wrist, arm-elbow, and arm-shoulder points are crucial for human arm motion, whereas during leg motion ankle-knee, knee-waist, and waist-ankle points are critical. These body junctions get different angle values during physical exercise, which helps us track and count the action. We assumed a simple, cheap and effective solution for multi-tracking these joints, which are marked with a distinctive color. Color filtering and color-based tracking steps are then performed to detect and count the actions by tracing the angle variations between joints. The developed application and performance evaluation tests show that our technique provides a reasonable performance while providing a simple and cheap video setup.en_US
dc.identifier.doi10.1007/978-3-319-26561-2_59
dc.identifier.endpage504en_US
dc.identifier.isbn978-3-319-26561-2
dc.identifier.isbn978-3-319-26560-5
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-84951870649en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage497en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-319-26561-2_59
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3272
dc.identifier.volume9492en_US
dc.identifier.wosWOS:000373889900059en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Int Publishing Agen_US
dc.relation.ispartofNeural Information Processing, Iconip 2015, Pt Iven_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHuman action detectionen_US
dc.subjectColor filteringen_US
dc.subjectColor trackingen_US
dc.titleCounting Human Actions in Video During Physical Exerciseen_US
dc.typeConference Objecten_US

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