%0 Conference Proceedings
%T Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data
%A Miguel Angel Bautista
%A Antonio Hernandez
%A Victor Ponce
%A Xavier Perez Sala
%A Xavier Baro
%A Oriol Pujol
%A Cecilio Angulo
%A Sergio Escalera
%B 21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis
%D 2012
%V 7854
%I Springer Berlin Heidelberg
%@ 0302-9743
%@ 978-3-642-40302-6
%F Miguel Angel Bautista2012
%O MILAB; OR;HuPBA;MV
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2120), last updated on Tue, 18 Oct 2016 13:28:42 +0200
%X Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data.
%U http://refbase.cvc.uab.es/files/BHP2012.pdf
%U http://dx.doi.org/10.1007/978-3-642-40303-3_14
%P 126-135