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  1. 教育学部・大学院教育学研究科
  2. その他 (教育学部)

Pose Estimation of Player's Hand with CNN for the Hand Pose Rally System

http://hdl.handle.net/10458/6521
http://hdl.handle.net/10458/6521
79be33e3-2611-49f8-9551-eab96d3ac07c
名前 / ファイル ライセンス アクション
p57_icste2018.pdf 本文 (576.7 kB)
Item type 会議発表論文 / Conference Paper(1)
公開日 2020-06-21
タイトル
タイトル Pose Estimation of Player's Hand with CNN for the Hand Pose Rally System
言語 en
言語
言語 eng
キーワード
言語 en
主題Scheme Other
主題 hand pose, convolution neural network, machine learning
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_5794
資源タイプ conference paper
著者 Hirakawa, Motoya

× Hirakawa, Motoya

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en Hirakawa, Motoya

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Suganuma, Akira

× Suganuma, Akira

WEKO 30985

en Suganuma, Akira

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抄録
内容記述タイプ Abstract
内容記述 Study on hand pause rally system has been taking place since 2012 in our laboratory. In this study, a method identifying the posture of a human hands has been developed. The conventional method has several problems. One of them is that accuracy does not readily rise. Therefore, it is necessary for us to build a method of identifying the posture by machine learning using a convolution neural network (CNN) which is an alternative method to the conventional discrimination method. As a result of the verification, the accuracy of the identification was about 40% overall in spite of a small learning data. Therefore, we processed the image data and artificially increased the amount of images. As a result of increasing the learning data and measuring it, the accuracy reached 54%. Currently we are studying ways to improve accuracy other than increasing the amount of images.
言語 en
書誌情報 en : International Conference on Science, Technology & Education (ICSTE 2018)

p. 57-60, 発行日 2018-09
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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Hirakawa, Motoya, Suganuma, Akira, 2018, Pose Estimation of Player's Hand with CNN for the Hand Pose Rally System: 57–60 p.

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