@inproceedings{discovery10115642,
       publisher = {Springer, Berlin, Heidelberg},
       booktitle = {International Workshop on Depth Image Analysis and Applications WDIA 2012: Advances in Depth Image Analysis and Applications},
          volume = {7854},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           pages = {126--135},
          series = {Lecture Notes in Computer Science book series},
            year = {2013},
           title = {Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data},
            issn = {1611-3349},
          author = {Bautista, M{\'A} and Hern{\'a}ndez-Vela, A and Ponce, V and Perez-Sala, X and Bar{\'o}, X and Pujol, O and Angulo, C and Escalera, S},
             url = {https://doi.org/10.1007/978-3-642-40303-3\%5f14},
        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.},
        keywords = {Depth maps, Gesture Recognition, Dynamic Time Warping, Statistical Pattern Recognition}
}