Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/101369
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dc.contributor.authorZhao, X.en
dc.contributor.authorLi, X.en
dc.contributor.authorPang, C.en
dc.contributor.authorSheng, Q.en
dc.contributor.authorWang, S.en
dc.contributor.authorYe, M.en
dc.date.issued2014en
dc.identifier.citationACM Transactions on Multimedia Computing, Communications and Applications, 2014; 11(1):22-1-22-18en
dc.identifier.issn1551-6857en
dc.identifier.issn1551-6865en
dc.identifier.urihttp://hdl.handle.net/2440/101369-
dc.description.abstractOnline human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. The recent introduction of cost-effective depth cameras brings a new trend of research on body-movement gesture recognition. However, there are two major challenges: (i) how to continuously detect gestures from unsegmented streams, and (ii) how to differentiate different styles of the same gesture from other types of gestures. In this article, we solve these two problems with a new effective and efficient feature extraction method—Structured Streaming Skeleton (SSS)—which uses a dynamic matching approach to construct a feature vector for each frame. Our comprehensive experiments on MSRC-12 Kinect Gesture, Huawei/3DLife-2013, and MSR-Action3D datasets have demonstrated superior performances than the state-of-the-art approaches. We also demonstrate model selection based on the proposed SSS feature, where the classifier of squared loss regression with l2,1 norm regularization is a recommended classifier for best performance.en
dc.description.statementofresponsibilityXin Zhao, Xue Li, Chaoyi Pang, Quan Z. Sheng, Sen Wang, Mao Yeen
dc.language.isoenen
dc.publisherAssociation for Computing Machineryen
dc.rights© 2014 ACMen
dc.subjectScene Analysis;motion; implementation; interactive systemsen
dc.titleStructured streaming skeleton - a new feature for online human gesture recognitionen
dc.typeJournal articleen
dc.identifier.rmid0030021100en
dc.identifier.doi10.1145/2648583en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP130104614en
dc.identifier.pubid153837-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS10en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Computer Science publications

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