Título:
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Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D
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Autor/a:
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Hernández-Vela, Antonio; Bautista Martín, Miguel Ángel; Perez-Sala, X.; Ponce López, Víctor; Escalera Guerrero, Sergio; Baró i Solé, Xavier; Pujol Vila, Oriol; Angulo Bahón, Cecilio
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Otros autores:
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Universitat de Barcelona |
Abstract:
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We 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. |
Materia(s):
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-Algorismes computacionals -Processos gaussians -Computer algorithms -Gaussian processes |
Derechos:
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(c) Elsevier B.V., 2014
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Tipo de documento:
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Artículo Artículo - Versión aceptada |
Editor:
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Elsevier B.V.
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