Approximate string matching: A lightweight approach to recognize gestures with Kinect
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- 作者：Rodrigo Ibañ ; ez rodrigo.ibanez@isistan.unicen.edu.ar" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Á ; lvaro Soria ; alvaro.soria@isistan.unicen.edu.ar" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Alfredo Teyseyre alfredo.teyseyre@isistan.unicen.edu.ar" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Guillermo Rodrí ; guez guillermo.rodriguez@isistan.unicen.edu.ar" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Marcelo Campo marcelo.campo@isistan.unicen.edu.ar" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Natural user interfaces ; Gesture recognition ; Machine learning ; Kinect ; Approximate string matching
- 刊名：Pattern Recognition
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：62
- 期：Complete
- 页码：73-86
- 全文大小：2357 K
文摘
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We proposed a lightweight approach to recognize gestures with Kinect, based on approximate string matching.
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We implemented two gesture recognition variants, based on string matching.
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We evaluated our approach by using the public MSRC-12 Kinect gesture dataset.
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We compared the accuracy and performance with other state-of-the-art gesture-recognition techniques.
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The experimental evaluations show that the proposed approach achieves better performance than the state-of-the-art algorithms.