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dc.contributor.authorKe, Q.
dc.contributor.authorBennamoun, M.
dc.contributor.authorAn, Senjian
dc.contributor.authorSohel, F.
dc.contributor.authorBoussaid, F.
dc.date.accessioned2018-08-08T04:44:14Z
dc.date.available2018-08-08T04:44:14Z
dc.date.created2018-08-08T03:50:34Z
dc.date.issued2017
dc.identifier.citationKe, Q. and Bennamoun, M. and An, S. and Sohel, F. and Boussaid, F. 2017. A new representation of skeleton sequences for 3D action recognition, 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 4570-4579.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/70274
dc.identifier.doi10.1109/CVPR.2017.486
dc.description.abstract

© 2017 IEEE. This paper presents a new method for 3D action recognition with skeleton sequences (i.e., 3D trajectories of human skeleton joints). The proposed method first transforms each skeleton sequence into three clips each consisting of several frames for spatial temporal feature learning using deep neural networks. Each clip is generated from one channel of the cylindrical coordinates of the skeleton sequence. Each frame of the generated clips represents the temporal information of the entire skeleton sequence, and incorporates one particular spatial relationship between the joints. The entire clips include multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We propose to use deep convolutional neural networks to learn long-term temporal information of the skeleton sequence from the frames of the generated clips, and then use a Multi-Task Learning Network (MTLN) to jointly process all frames of the generated clips in parallel to incorporate spatial structural information for action recognition. Experimental results clearly show the effectiveness of the proposed new representation and feature learning method for 3D action recognition.

dc.titleA new representation of skeleton sequences for 3D action recognition
dc.typeConference Paper
dcterms.source.volume2017-January
dcterms.source.startPage4570
dcterms.source.endPage4579
dcterms.source.titleProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
dcterms.source.seriesProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
dcterms.source.isbn9781538604571
dcterms.source.conference30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
dcterms.source.placeUSA
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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