ŞEHİR e-arşiv

Gesture recognition using skeleton data with weighted dynamic time warping

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dc.contributor.author Çelebi, Sait
dc.contributor.author Temiz, Talha T.
dc.contributor.author Arıcı, Tarık
dc.contributor.author Aydın, Ali S.
dc.date.accessioned 2016-07-22T13:44:46Z
dc.date.available 2016-07-22T13:44:46Z
dc.date.issued 2013
dc.identifier.isbn 978-989856547-1
dc.identifier.uri http://hdl.handle.net/11498/31216
dc.description.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-of-the- art. en_US
dc.language.iso eng en_US
dc.rights info:eu-repo/semantics/embargoedAccess en_US
dc.subject Gesture Recognition en_US
dc.subject Template-Matching Algorithms en_US
dc.subject Human and Computer en_US
dc.subject Hareket Tanıma en_US
dc.subject Şablon Eşleştirme Algoritmaları en_US
dc.subject İnsan ve Bilgisayar en_US
dc.title Gesture recognition using skeleton data with weighted dynamic time warping en_US
dc.type Article en_US
dc.relation.journal 8th International Conference on Computer Vision Theory and Applications en_US

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