%L discovery10115642
%K Depth maps, Gesture Recognition, Dynamic Time Warping, Statistical Pattern Recognition
%I Springer, Berlin, Heidelberg
%S Lecture Notes in Computer Science book series
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%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.
%B International Workshop on Depth Image Analysis and Applications WDIA 2012: Advances in Depth Image Analysis and Applications
%V 7854
%A MÁ Bautista
%A A Hernández-Vela
%A V Ponce
%A X Perez-Sala
%A X Baró
%A O Pujol
%A C Angulo
%A S Escalera
%T Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data
%D 2013
%P 126-135