@InProceedings{MiguelAngelBautista2012,
author="Miguel Angel Bautista
and Antonio Hernandez
and Victor Ponce
and Xavier Perez Sala
and Xavier Baro
and Oriol Pujol
and Cecilio Angulo
and Sergio Escalera",
title="Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data",
booktitle="21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis",
year="2012",
publisher="Springer Berlin Heidelberg",
volume="7854",
pages="126--135",
abstract="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.",
optnote="MILAB; OR;HuPBA;MV",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2120), last updated on Tue, 18 Oct 2016 13:28:42 +0200",
isbn="978-3-642-40302-6",
issn="0302-9743",
doi="10.1007/978-3-642-40303-3_14",
file=":http://refbase.cvc.uab.es/files/BHP2012.pdf:PDF"
}