Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era
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 recogni...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns.2023.2.01291 |
| _version_ | 1797340623476359168 |
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| author | Yang Jingming |
| author_facet | Yang Jingming |
| author_sort | Yang Jingming |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:05:47Z |
| format | Article |
| id | doaj.art-88b39b6040694a0293a0b89bdca7fe66 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:05:47Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-88b39b6040694a0293a0b89bdca7fe662024-01-29T08:52:41ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.01291Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia EraYang Jingming01Zhengzhou Technology and Business University, Zhengzhou, Henan, 450000, China.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.https://doi.org/10.2478/amns.2023.2.01291kinectmotion recognition technology3d cnnsdance talent developmentskeleton information78a48 |
| spellingShingle | Yang Jingming Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era Applied Mathematics and Nonlinear Sciences kinect motion recognition technology 3d cnns dance talent development skeleton information 78a48 |
| title | Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era |
| title_full | Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era |
| title_fullStr | Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era |
| title_full_unstemmed | Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era |
| title_short | Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era |
| title_sort | research on the application of dance talent cultivation mode in colleges and universities in the context of multimedia era |
| topic | kinect motion recognition technology 3d cnns dance talent development skeleton information 78a48 |
| url | https://doi.org/10.2478/amns.2023.2.01291 |
| work_keys_str_mv | AT yangjingming researchontheapplicationofdancetalentcultivationmodeincollegesanduniversitiesinthecontextofmultimediaera |