Bautista, MÁ;
Hernández-Vela, A;
Ponce, V;
Perez-Sala, X;
Baró, X;
Pujol, O;
Angulo, C;
(2013)
Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data.
In:
International Workshop on Depth Image Analysis and Applications WDIA 2012: Advances in Depth Image Analysis and Applications.
(pp. pp. 126-135).
Springer, Berlin, Heidelberg
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data |
| Event: | International Workshop on Depth Image Analysis and Applications 2012 |
| ISBN-13: | 9783642403026 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-642-40303-3_14 |
| Publisher version: | https://doi.org/10.1007/978-3-642-40303-3_14 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Depth maps, Gesture Recognition, Dynamic Time Warping, Statistical Pattern Recognition |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10115642 |
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