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View-independent human action recognition with Volume Motion Template on single stereo camera

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dc.contributor.authorRoh, Myung-Cheol-
dc.contributor.authorShin, Ho-Keun-
dc.contributor.authorLee, Seong-Whan-
dc.date.accessioned2021-09-08T03:11:28Z-
dc.date.available2021-09-08T03:11:28Z-
dc.date.created2021-06-11-
dc.date.issued2010-05-01-
dc.identifier.issn0167-8655-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/116468-
dc.description.abstractVision-based human action recognition provides an advanced interface, and research in the field of human action recognition has been actively carried out. However, an environment from dynamic viewpoint, where we can be in any position, any direction, etc., must be considered in our living 3D space. In order to overcome the viewpoint dependency, we propose a Volume Motion Template (VMT) and Projected Motion Template (PMT). The proposed VMT method is an extension of the Motion History Image (MHI) method to 3D space. The PMT is generated by projecting the VMT into a 2D plane that is orthogonal to an optimal virtual viewpoint where the optimal virtual viewpoint is a viewpoint from which an action can be described in greatest detail, in 2D space. From the proposed method, any actions taken from different viewpoints can be recognized independent of the viewpoints. The experimental results demonstrate the accuracies and effectiveness of the proposed VMT method for view-independent human action recognition. (C) 2009 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER-
dc.subjectTIME GESTURE RECOGNITION-
dc.subjectSEGMENTATION-
dc.titleView-independent human action recognition with Volume Motion Template on single stereo camera-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, Seong-Whan-
dc.identifier.doi10.1016/j.patrec.2009.11.017-
dc.identifier.scopusid2-s2.0-77649337452-
dc.identifier.wosid000276700500013-
dc.identifier.bibliographicCitationPATTERN RECOGNITION LETTERS, v.31, no.7, pp.639 - 647-
dc.relation.isPartOfPATTERN RECOGNITION LETTERS-
dc.citation.titlePATTERN RECOGNITION LETTERS-
dc.citation.volume31-
dc.citation.number7-
dc.citation.startPage639-
dc.citation.endPage647-
dc.type.rimsART-
dc.type.docTypeArticle; Proceedings Paper-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.subject.keywordPlusTIME GESTURE RECOGNITION-
dc.subject.keywordPlusSEGMENTATION-
dc.subject.keywordAuthorView-independence-
dc.subject.keywordAuthorHuman action recognition-
dc.subject.keywordAuthorVolume Motion Template-
dc.subject.keywordAuthorMotion History Image-
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