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DC poleHodnotaJazyk
dc.contributor.authorAhmed, Faisal
dc.contributor.authorPolash Paul, Padma
dc.contributor.authorGavrilova, Marina L.
dc.contributor.editorSkala, Václav
dc.date.accessioned2016-01-07T08:36:40Z
dc.date.available2016-01-07T08:36:40Z
dc.date.issued2015
dc.identifier.citationJournal of WSCG. 2015, vol. 23, no. 1, p. 147-156.en
dc.identifier.issn1213–6972 (hardcopy)
dc.identifier.issn1213–6980 (CD-ROM)
dc.identifier.issn1213–6964 (online)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2015/!_2015_Journal_WSCG-No-2.pdf
dc.identifier.urihttp://hdl.handle.net/11025/17149
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectrozpoznávání chůzecs
dc.subjectKinect v2cs
dc.subjectJRAcs
dc.subjectDTW-kernelcs
dc.subjectanalýza pohybucs
dc.titleKinect-based gait recognition using sequences of the most relevant joint relative anglesen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper introduces a new 3D skeleton-based gait recognition method for motion captured by a low-cost consumer level camera, namely the Kinect. We propose a new representation of human gait signature based on the spatio-temporal changes in relative angles among different skeletal joints with respect to a reference point. A sequence of joint relative angles (JRA) between two skeletal joints, computed over a complete gait cycle, comprises an intuitive representation of the relative motion patterns of the involved joints. JRA sequences originated from different joint pairs are then evaluated to find the most relevant JRAs for gait description. We also introduce a new dynamic time warping (DTW)-based kernel that takes the collection of the most relevant JRA sequences from the train and test samples and computes a dissimilarity measure. The use of DTW in the proposed kernel makes it robust in respect to variable walking speed and thus eliminates the need of resampling to obtain equal-length feature vectors. The performance of the proposed method was evaluated using a Kinect skeletal gait database. Experimental results show that the proposed method can more effectively represent and recognize human gait, as compared against some other Kinect-based gait recognition methods.en
dc.subject.translatedgait recognitionen
dc.subject.translatedKinect v2en
dc.subject.translatedJRAen
dc.subject.translatedDTW-kernelen
dc.subject.translatedmotion analysisen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 23, Number 2 (2015)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 23 (2015)
  6. Volume 23, Number 2 (2015)