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Title: Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D
Author: Hernández-Vela, Antonio
Bautista Martín, Miguel Ángel
Perez-Sala, Xavier
Ponce López, Víctor
Escalera Guerrero, Sergio
Baró i Solé, Xavier
Pujol Vila, Oriol
Angulo Bahón, Cecilio
Keywords: Algorismes computacionals
Processos gaussians
Computer algorithms
Gaussian processes
Issue Date: 6-Sep-2014
Publisher: Elsevier B.V.
Abstract: 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.
Note: Versió postprint del document publicat a:
It is part of: Pattern Recognition Letters, 2014, vol. 50, p. 112-121
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ISSN: 0167-8655
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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