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dc.contributor.authorQuirion, Sébastien
dc.contributor.authorBranzan-Albu, Alexandra
dc.contributor.authorBergevin, Robert
dc.contributor.editorSkala, Václav
dc.date.accessioned2014-05-22T08:48:05Z-
dc.date.available2014-05-22T08:48:05Z-
dc.date.issued2005
dc.identifier.citationWSCG '2005: Short Papers: The 13-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2005 in co-operation with EUROGRAPHICS, p. 145-148.en
dc.identifier.isbn80-903100-9-5
dc.identifier.urihttp://wscg.zcu.cz/WSCG2005/Papers_2005/Short/!WSCG2005_Short_Proceedings_Final.pdf
dc.identifier.urihttp://hdl.handle.net/11025/11230
dc.description.abstractThis paper presents a new multi-step, skeleton-based approach for the temporal segmentation of human activities from video sequences. Several signals are first extracted from a skeleton sequence. These signals are then segmented individually to localize their cyclic segments. Finally, all individual segmentations are merged with respect to the global set of signals. Our approach requires no prior knowledge on human activities and can use any generic stick-model. Two different techniques for signal segmentation and for the fusion of the individual segmentations are proposed and tested on a database of fifteen video sequences of variable level of complexity.en
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.relation.ispartofseriesWSCG '2005: Short Papersen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectporozumění videucs
dc.subjectanalýza pohybucs
dc.subjectanalýza periodicitycs
dc.subjectsegmentace signálucs
dc.subjectdatová fúzecs
dc.titleSkeleton-based temporal segmentation of human activities from video sequencesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedvideo understandingen
dc.subject.translatedmotion analysisen
dc.subject.translatedperiodicity analysisen
dc.subject.translatedsignal segmentationen
dc.subject.translateddata fusionen
dc.type.statusPeer-revieweden
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