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<p>This paper combines Kinect and convolutional neural networks to construct a dance movement recognition technology based on 3D CNNs. Applying dance movement recognition technology to dance teaching builds a new mode of training dance talents in colleges and universities. The role of Kinect in recognizing dance movements is explored from three aspects: real-time motion capture, human skeleton tracking, and information input. The dance movement image is computerized by calculating the depth of the points using stereo analysis. To analyze dance movements and classify them by features, a convolutional neural network is combined. Based on two-dimensional convolutional neural networks, three-dimensional convolutional neural networks have been constructed, which improve the comprehensiveness of dance movement information. By combining dance movement recognition technology with dance talent cultivation, we analyze the students’ professional dance ability and the teaching effect under the new talent cultivation mode. The results show that the teaching effect of the dance talent cultivation mode combined with the movement recognition technology is better, and the percentage of students dance movements reaching the standard in a movement completion is 0.95. The professional ability of dance talent is 0.8 percent.</p>
</abstract>

This paper combines Kinect and convolutional neural networks to construct a dance movement recognition technology based on 3D CNNs. Applying dance movement recognition technology to dance teaching builds a new mode of training dance talents in colleges and universities. The role of Kinect in recognizing dance movements is explored from three aspects: real-time motion capture, human skeleton tracking, and information input. The dance movement image is computerized by calculating the depth of the points using stereo analysis. To analyze dance movements and classify them by features, a convolutional neural network is combined. Based on two-dimensional convolutional neural networks, three-dimensional convolutional neural networks have been constructed, which improve the comprehensiveness of dance movement information. By combining dance movement recognition technology with dance talent cultivation, we analyze the students’ professional dance ability and the teaching effect under the new talent cultivation mode. The results show that the teaching effect of the dance talent cultivation mode combined with the movement recognition technology is better, and the percentage of students dance movements reaching the standard in a movement completion is 0.95. The professional ability of dance talent is 0.8 percent.

Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era

Zhengzhou Technology and Business University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper combines Kinect and convolutional neural networks to construct a dance movement recognition technology based on 3D CNNs. Applying dance movement...

Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era

Data publikacji: 29 lis 2023

Zakres stron: -

Otrzymano: 27 sty 2023

Przyjęty: 09 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01291

Słowa kluczowe
Kinect, Motion recognition technology, 3D CNNs, Dance talent development, Skeleton information

© 2023 Jingming Yang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era

Data publikacji: 29 lis 2023

Zakres stron: -

Otrzymano: 27 sty 2023

Przyjęty: 09 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01291

Słowa kluczoweKinect, Motion recognition technology, 3D CNNs, Dance talent development, Skeleton information

© 2023 Jingming Yang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Kinect, Motion recognition technology, 3D CNNs, Dance talent development, Skeleton information