Recognition of typical skills and movements of classical dance based on attention machine graph convolutional network

DOI

10.2991/978-94-6463-417-4_53How to use a DOI?

Keywords

attention mechanism; Action recognition; Typical Techniques and Actions of Chinese Classical Dance

Abstract

Classical dance is a form of dance with a profound historical and cultural heritage, and its techniques and movements are crucial for the performance and skill level of dancers. With the development of artificial intelligence technology, graph convolutional networks based on attention mechanisms have become an effective tool for identifying and analyzing typical techniques and movements in classical dance. This technology helps students improve their skills by capturing key movements of dancers, providing real-time feedback and personalized guidance. Teachers can use this to evaluate student performance, adjust teaching content and methods, and promote the progress of dance education. This article aims to explore the recognition of typical movements in classical dance using graph convolutional networks based on attention mechanisms.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhen Liu
AU  - Yimei Zhu
PY  - 2024
DA  - 2024/05/07
TI  - Recognition of typical skills and movements of classical dance based on attention machine graph convolutional network
BT  - Proceedings of the 2024 5th International Conference on Big Data and Informatization Education (ICBDIE 2024)
PB  - Atlantis Press
SP  - 567
EP  - 573
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-417-4_53
DO  - 10.2991/978-94-6463-417-4_53
ID  - Liu2024
ER  -