Recognition of Movement Features of Different Ethnic Dances: Research on Motion Image Information

Authors

  • Fan Yang Yan’an University, Yan’an, Shaanxi 716000, China

Abstract

The inheritance and teaching of ethnic dances can be innovated by introducing digital technology. This paper studied the recognition of movement features of different ethnic dances. It optimized the spatiotemporal graph convolutional network (ST-GCN) based on skeleton information in motion images, developed a multi-stream ST-GCN (MS-STGCN) model, and conducted experiments using NTU-RGB+D60/120 datasets and a self-constructed ethnic dance dataset. The results showed that for the NTU-RGB+D 60 dataset, the accuracy of the MS-STGCN model was 92.6% (X-Sub) and 96.8% (X-View); for the NTU-RGB+D 120 dataset, its accuracy was 89.7% (X-Sub) and 89.9% (X-Set), outperforming the other algorithms; it achieved an accuracy of 90.6% for the ethnic dance dataset. The findings demonstrate the reliability of the MS-STGCN model in recognizing the features of ethnic dance movements, which can be further applied in practice.

Keywords: spatiotemporal graph convolutional network; ethnic dance; motion image; recognition

Cite As

F. Yang, "Recognition of Movement Features of Different Ethnic Dances: Research on Motion Image Information",
Engineering Intelligent Systems, vol. 34 no. 5, pp. 647-652, 2026.






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Published

2026-09-01

Issue

Vol. 34 No. 5 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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