Design and Implementation of Dance Motion Capture System Based on Deep Learning Algorithm
This paper is devoted to the design and implementation of a dance motion capture system based on deep learning algorithm, aiming at improving the accuracy and real-time performance of dancers' motion capture. By introducing advanced deep learning technology, especially the Mask R-CNN algorithm,...
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| Veröffentlicht in: | 2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC) S. 153 - 157 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
29.12.2023
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| Online-Zugang: | Volltext |
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| Zusammenfassung: | This paper is devoted to the design and implementation of a dance motion capture system based on deep learning algorithm, aiming at improving the accuracy and real-time performance of dancers' motion capture. By introducing advanced deep learning technology, especially the Mask R-CNN algorithm, we successfully solved the common problems of complex posture and occlusion in traditional dance motion capture systems. The system can accurately capture the movements of various parts of the dancer's body, and restore the dance movements in 3D space, which provides a more accurate expression and creation platform for dancers. By optimizing the algorithm and hardware, we realized the real-time capture and analysis of different dance styles and action types, which provided a more flexible and diverse training and performance environment for dancers. This study provides strong support for the development of dance motion capture technology, provides advanced and practical tools for dancers, educators and researchers, and promotes the innovation and progress of dance art. |
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| DOI: | 10.1109/ICIRDC62824.2023.00033 |