PT Unknown
AU Miguel Angel Bautista
   Antonio Hernandez
   Victor Ponce
   Xavier Perez Sala
   Xavier Baro
   Oriol Pujol
   Cecilio Angulo
   Sergio Escalera
TI Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data
BT 21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis
PY 2012
BP 126
EP 135
VL 7854
DI 10.1007/978-3-642-40303-3_14
AB 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.
ER