WEKO3
アイテム
Pose Estimation of Player's Hand with CNN for the Hand Pose Rally System
http://hdl.handle.net/10458/6521
http://hdl.handle.net/10458/652195450cfc-3aba-4d0f-a4cc-a1a94bfa6f36
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
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× Suganuma, Akira |
|||||
| 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 | |||||
