Hernandez-Vela, A;
Angel Bautista, M;
Perez-Sala, X;
Ponce-Lopez, V;
Escalera, S;
Baro, X;
Pujol, O;
(2014)
Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D.
Pattern Recognition Letters
, 50
pp. 112-121.
10.1016/j.patrec.2013.09.009.
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Abstract
We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach.
| Type: | Article |
|---|---|
| Title: | Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.patrec.2013.09.009 |
| Publisher version: | https://doi.org/10.1016/j.patrec.2013.09.009 |
| 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: | RGB-D, Bag-of-Words, Dynamic Time Warping, Human Gesture, 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/10114951 |
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