| Project/Area Number |
18K02893
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 09070:Educational technology-related
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| Research Institution | Ochanomizu University |
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Principal Investigator |
NAKAMURA MINAKO お茶の水女子大学, 基幹研究院, 准教授 (20345408)
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| Co-Investigator(Kenkyū-buntansha) |
芝野 耕司 東京外国語大学, その他部局等, 名誉教授 (50216024)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 民族舞踊学 / モーションキャプチャ / Labanotation / OpenPose / Open Pose / データベース / 深層学習 / 舞踊教育 / 舞踊動作分析 |
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| Outline of Final Research Achievements |
We collected motion capture data in the multimodal database of Carnegie Mellon University, which is available on the Internet, and investigated papers presented at international dance-related conferences to obtain motion capture data, but we were unable to collect sufficient data. This is due to the fact that motion capture requires a lot of time and effort.
In 2017, Open Pose, which obtains motion data by applying deep learning to video data from CMU, was developed, making it possible to obtain motion data from a large amount of video data available on the Internet.
The research method was changed to research utilizing these results, and a new proposal for Grant-in-Aid for Scientific Research(B） (21H03771) was made.
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| Academic Significance and Societal Importance of the Research Achievements |
現在先端研究分野で最も注目を集めているDeep Learningを用いて，これまでDeep LearningやAIが適用されることの殆どなかった動作認識分野を開拓する点がこの研究の独創的な点である。Deep Learningを同時に比較民族舞踊研究の視点から各民族舞踊の特徴抽出を行うことによって，舞踊のモーションキャプチャデータからの内在的分析を可能とすることによって，量的に基盤を持つ舞踊研究を可能とするとともに，より客観的な舞踊研究への道を拓く。
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