Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced Framework
This study presents the implementation of an AI-based prototype for recognizing Korean traditional dance movements using a metadata-enhanced dataset. The research was conducted in three stages. First, a classification framework was developed to reflect the unique characteristics of Korean traditiona...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/1/409 |
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| Summary: | This study presents the implementation of an AI-based prototype for recognizing Korean traditional dance movements using a metadata-enhanced dataset. The research was conducted in three stages. First, a classification framework was developed to reflect the unique characteristics of Korean traditional dance. Second, video data were collected from existing and newly filmed sources, and a metadata set was created by labeling five fundamental movements for training. Third, the BlazePose model was applied to generate real-time skeletal key points, which were integrated with the metadata-enhanced dataset and processed using a customized approach to recognize dance movements in real time. The developed prototype successfully recognizes five fundamental Korean traditional dance movements and demonstrates the potential of AI technology in analyzing complex motion patterns. By integrating existing AI models with a domain-specific dataset, this study provides a systematic approach to the digital preservation and modern reinterpretation of traditional arts. Furthermore, the methodology can be extended to recognize dance movements from other cultures, offering new possibilities for the preservation and transmission of intangible cultural heritage through digital technology. |
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| ISSN: | 2076-3417 |