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Authors: Sait Celebi 1 ; Talha T. Temiz 2 ; Ali S. Aydin 2 and Tarik Arici 2

Affiliations: 1 Istanbul Sehir University, Turkey ; 2 College of Engineering and Natural Sciences and Istanbul Sehir University, Turkey

Keyword(s): Gesture Recognition, Dynamic Time Warping, Kinect.

Abstract: With Microsoft’s launch of Kinect in 2010, and release of Kinect SDK in 2011, numerous applications and research projects exploring new ways in human-computer interaction have been enabled. Gesture recognition is a technology often used in human-computer interaction applications. Dynamic time warping (DTW) is a template matching algorithm and is one of the techniques used in gesture recognition. To recognize a gesture, DTW warps a time sequence of joint positions to reference time sequences and produces a similarity value. However, all body joints are not equally important in computing the similarity of two sequences. We propose a weighted DTW method that weights joints by optimizing a discriminant ratio. Finally, we demonstrate the recognition performance of our proposed weighted DTW with respect to the conventional DTW and state-ofthe-art.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Celebi, S.; Temiz, T.; Aydin, A. and Arici, T. (2013). Gesture Recognition using Skeleton Data with Weighted Dynamic Time Warping. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP; ISBN 978-989-8565-47-1; ISSN 2184-4321, SciTePress, pages 620-625. DOI: 10.5220/0004217606200625

@conference{visapp13,
author={Sait Celebi. and Talha T. Temiz. and Ali S. Aydin. and Tarik Arici.},
title={Gesture Recognition using Skeleton Data with Weighted Dynamic Time Warping},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP},
year={2013},
pages={620-625},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004217606200625},
isbn={978-989-8565-47-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP
TI - Gesture Recognition using Skeleton Data with Weighted Dynamic Time Warping
SN - 978-989-8565-47-1
IS - 2184-4321
AU - Celebi, S.
AU - Temiz, T.
AU - Aydin, A.
AU - Arici, T.
PY - 2013
SP - 620
EP - 625
DO - 10.5220/0004217606200625
PB - SciTePress