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dc.contributor.authorShamsipour, G.
dc.contributor.authorShanbehzadeh, J.
dc.contributor.authorSarrafzadeh, Hossein
dc.contributor.editorS. I. Ao., O. Castillo., C. Douglas., D. D. Feng & A. M. Korsunsky
dc.date.accessioned2017-07-25T21:14:09Z
dc.date.available2017-07-25T21:14:09Z
dc.date.issued2017-03
dc.identifier.isbn9789881404732
dc.identifier.issn2078-0958
dc.identifier.issn2078-0966
dc.identifier.urihttps://hdl.handle.net/10652/3877
dc.description.abstractHuman action recognition is the process of labeling a video according to human behavior. This process requires a large set of labeled video and analyzing all the frames of a video. The consequence is high computation and memory requirement. This paper solves these problems by focusing on a limited set rather than all the human action and considering the human-object interaction. This paper employs three randomly selected video frames instead of employing all the frames and, Convolutional Neural Network extracts conceptual features and recognize the video objects. Finally, support vector machine determines the relation between these objects and labels the video. The proposed method have been tested on two popular datasets ; UCF Sports Action and Olympic Sports. The results show improvements over state-of-the-art algorithms. This work is the outcome of Shamsipour's M.Sc thesis at Kharazmi University.en_NZ
dc.language.isoenen_NZ
dc.publisherNewswood and International Association of Engineersen_NZ
dc.relation.urihttp://www.iaeng.org/publication/IMECS2017/en_NZ
dc.relation.uriwww.iaeng.org/publication/IMECS2017/IMECS2017_pp7-13.pdfen_NZ
dc.subjectcomputer visionen_NZ
dc.subjecthuman activity recognitionen_NZ
dc.subjectconvolutional neural networks (CNN)en_NZ
dc.subjectsupport vector machineen_NZ
dc.titleHuman action recognition by conceptual featuresen_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.date.updated2017-06-01T14:30:08Z
dc.rights.holderAuthorsen_NZ
dc.subject.marsden080109 Pattern Recognition and Data Miningen_NZ
dc.identifier.bibliographicCitationShamsipour, G., Shanbehzadeh, J., & Sarrafzadeh, A. (2017, March). Human action recognition by conceptual features. S. I. Ao., O. Castillo., C. Douglas., D. D. Feng & A. M. Korsunsky (Ed.), International MultiConference of Engineers and Computer Scientists 2017 (IMECS2017) (pp.online). 1.en_NZ
unitec.publication.spage7en_NZ
unitec.publication.lpage13en_NZ
unitec.publication.volume1en_NZ
unitec.publication.titleProceedings of the International MultiConference of Engineers and Computer Scientists 2017 (IMECS2017)en_NZ
unitec.conference.titleInternational MultiConference of Engineers and Computer Scientists 2017 (IMECS2017)en_NZ
unitec.conference.orgInternational MultiConference of Engineers and Computer Scientistsen_NZ
unitec.conference.locationHong Kong, Chinaen_NZ
unitec.conference.sdate2017-03-15
unitec.conference.edate2017-03-17
unitec.peerreviewedyesen_NZ
unitec.identifier.roms59838en_NZ


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