TY  - GEN
N2  - 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.
ID  - discovery10115642
UR  - https://doi.org/10.1007/978-3-642-40303-3_14
EP  - 135
T3  - Lecture Notes in Computer Science book series
Y1  - 2013///
SN  - 1611-3349
TI  - Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data
AV  - public
PB  - Springer, Berlin, Heidelberg
A1  - Bautista, MÁ
A1  - Hernández-Vela, A
A1  - Ponce, V
A1  - Perez-Sala, X
A1  - Baró, X
A1  - Pujol, O
A1  - Angulo, C
A1  - Escalera, S
KW  - Depth maps
KW  -  Gesture Recognition
KW  -  Dynamic Time Warping
KW  -  Statistical Pattern Recognition
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SP  - 126
ER  -