Online human gesture recognition from motion data streams

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dc.contributor.authorZhao, X.
dc.contributor.authorLi, X.
dc.contributor.authorPang, C.
dc.contributor.authorZhu, X.
dc.contributor.authorSheng, Q.
dc.contributor.conferenceInternational Conference on Multimedia (21st : 2013 : Barcelona)
dc.date.issued2013
dc.description.abstractOnline human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the state-of-the-art approaches.
dc.description.statementofresponsibilityXin Zhao, Xue Li, Chaoyi Pang, Xiaofeng Zhu, Quan Z. Sheng
dc.identifier.citationProceedings of the 2013 21st ACM Multimedia Conference, 2013 / pp.23-32
dc.identifier.doi10.1145/2502081.2502103
dc.identifier.isbn9781450324045
dc.identifier.urihttp://hdl.handle.net/2440/83773
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.publisher.placeSpain
dc.rightsCopyright 2013 ACM
dc.source.urihttps://doi.org/10.1145/2502081.2502103
dc.subjectGesture Recognition
dc.subjectFeature Extraction
dc.subjectDepth Camera
dc.titleOnline human gesture recognition from motion data streams
dc.typeConference paper
pubs.publication-statusPublished

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