%0 Generic
%A Bautista, MÁ
%A Hernández-Vela, A
%A Ponce, V
%A Perez-Sala, X
%A Baró, X
%A Pujol, O
%A Angulo, C
%A Escalera, S
%D 2013
%F discovery:10115642
%I Springer, Berlin, Heidelberg
%K Depth maps, Gesture Recognition, Dynamic Time Warping, Statistical Pattern Recognition
%P 126-135
%T Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data
%U https://discovery.ucl.ac.uk/id/eprint/10115642/
%V 7854
%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.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.