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dc.contributor.authorHernández-Vela, Antonio-
dc.contributor.authorBautista Martín, Miguel Ángel-
dc.contributor.authorPerez-Sala, Xavier-
dc.contributor.authorPonce López, Víctor-
dc.contributor.authorEscalera Guerrero, Sergio-
dc.contributor.authorBaró i Solé, Xavier-
dc.contributor.authorPujol Vila, Oriol-
dc.contributor.authorAngulo Bahón, Cecilio-
dc.description.abstractWe present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach.-
dc.format.extent10 p.-
dc.publisherElsevier B.V.-
dc.relation.isformatofVersió postprint del document publicat a:
dc.relation.ispartofPattern Recognition Letters, 2014, vol. 50, p. 112-121-
dc.rights(c) Elsevier B.V., 2014-
dc.sourceArticles publicats en revistes (Matemàtiques i Informàtica)-
dc.subject.classificationAlgorismes computacionals-
dc.subject.classificationProcessos gaussians-
dc.subject.otherComputer algorithms-
dc.subject.otherGaussian processes-
dc.titleProbability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D-
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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