%0 Journal Article
%T Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D
%A Antonio Hernandez
%A Miguel Angel Bautista
%A Xavier Perez Sala
%A Victor Ponce
%A Sergio Escalera
%A Xavier Baro
%A Oriol Pujol
%A Cecilio Angulo
%J Pattern Recognition Letters
%D 2014
%V 50
%N 1
%F Antonio Hernandez2014
%O HuPBA;MV; 605.203
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2353), last updated on Wed, 16 Jan 2019 09:24:35 +0100
%X 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.
%K RGB-D
%K Bag-of-Words
%K Dynamic Time Warping
%K Human Gesture Recognition
%U http://refbase.cvc.uab.es/files/HBP2014.pdf
%U http://dx.doi.org/10.1016/j.patrec.2013.09.009
%P 112-121