The application of pose estimation algorithm in ballet movement analysis

Jia Chen

doi:10.1117/12.3026811

15 March 2024 The application of pose estimation algorithm in ballet movement analysis

Jia Chen

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751X (2024) https://doi.org/10.1117/12.3026811
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

This paper studies the application of pose estimation algorithm in ballet movement analysis. In view of the complexity and diversity of ballet movements, traditional analysis methods often fail to accurately capture key movement and posture information. Therefore, in this study, a new pose estimation algorithm was proposed based on key point correlation modeling and full-view data expansion techniques, and a deep neural network structure was used to extract and classify ballet movements. Through the training network, the input ballet movement data is converted into key point coordinates and the exact position and pose information for each key point is predicted. Experimental results show that compared with traditional methods, the new pose estimation algorithm based on key point correlation modeling and full- view data expansion can more accurately capture the details and features of ballet movements, and the correct rate of pose analysis is increased by 7.76%. In summary, the pose estimation algorithm based on key point correlation modeling and full view data expansion technology provides an effective solution for ballet movement analysis. In the future, the algorithm is expected to play an important role in the fields of dance teaching, dance evaluation and art research.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jia Chen "The application of pose estimation algorithm in ballet movement analysis", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751X (15 March 2024); https://doi.org/10.1117/12.3026811

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KEYWORDS

Pose estimation

Motion analysis

Evolutionary algorithms

Data modeling

Education and training

Analytical research

Modeling

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