@Article{AntonioHernandez2014,
author="Antonio Hernandez
and Miguel Angel Bautista
and Xavier Perez Sala
and Victor Ponce
and Sergio Escalera
and Xavier Baro
and Oriol Pujol
and Cecilio Angulo",
title="Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D",
journal="Pattern Recognition Letters",
year="2014",
volume="50",
number="1",
pages="112--121",
optkeywords="RGB-D",
optkeywords="Bag-of-Words",
optkeywords="Dynamic Time Warping",
optkeywords="Human Gesture Recognition",
abstract="PATREC5825We 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.",
optnote="HuPBA;MV; 605.203",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2353), last updated on Wed, 16 Jan 2019 09:24:35 +0100",
doi="10.1016/j.patrec.2013.09.009",
file=":http://refbase.cvc.uab.es/files/HBP2014.pdf:PDF"
}