The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks
With the rapid development of artificial intelligence and computer vision technology, dance action recognition and quality assessment have become a challenging and important research field. Traditional dance movement recognition methods often rely on manual feature extraction and expert scoring, whi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014509 |
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| author | Wei Qin Junying Meng |
| author_facet | Wei Qin Junying Meng |
| author_sort | Wei Qin |
| collection | DOAJ |
| description | With the rapid development of artificial intelligence and computer vision technology, dance action recognition and quality assessment have become a challenging and important research field. Traditional dance movement recognition methods often rely on manual feature extraction and expert scoring, which have problems such as strong subjectivity, low efficiency, and difficulty in handling complex movements. To address these issues, this study proposes a dance action recognition and quality assessment method based on Spatio Temporal Convolutional Neural Network (ST-CNN). Specifically, it includes a feature extraction network based on 3D spatiotemporal description operators and a multi-scale aggregated long short memory network for extracting spatial features of single frame images and fusing video sequence features composed of all frames. This approach can effectively capture dynamic changes in dance movements by combining information from spatial and temporal dimensions, thereby achieving more accurate recognition and evaluation. In addition, to address the high storage and computational requirements of convolutional neural networks during training, we integrated dance video sequences into an IoT architecture to address parallel recognition and evaluation of multiple videos. Finally, we validated the proposed method on the public datasets NTU-RGB 60 and NTU-RGB 120, and the experimental results demonstrated the effectiveness of the method. |
| format | Article |
| id | doaj-art-fb9d90def1fc4666b37cb9405394f45a |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-fb9d90def1fc4666b37cb9405394f45a2024-11-29T06:23:05ZengElsevierAlexandria Engineering Journal1110-01682025-02-011144654The research on dance motion quality evaluation based on spatiotemporal convolutional neural networksWei Qin0Junying Meng1College of Music and Dance, North Minzu University, 750021, Yinchuan, ChinaCollege of Future Information Technology, Shijiazhuang University, 050035, Shijiazhuang, China; Corresponding author.With the rapid development of artificial intelligence and computer vision technology, dance action recognition and quality assessment have become a challenging and important research field. Traditional dance movement recognition methods often rely on manual feature extraction and expert scoring, which have problems such as strong subjectivity, low efficiency, and difficulty in handling complex movements. To address these issues, this study proposes a dance action recognition and quality assessment method based on Spatio Temporal Convolutional Neural Network (ST-CNN). Specifically, it includes a feature extraction network based on 3D spatiotemporal description operators and a multi-scale aggregated long short memory network for extracting spatial features of single frame images and fusing video sequence features composed of all frames. This approach can effectively capture dynamic changes in dance movements by combining information from spatial and temporal dimensions, thereby achieving more accurate recognition and evaluation. In addition, to address the high storage and computational requirements of convolutional neural networks during training, we integrated dance video sequences into an IoT architecture to address parallel recognition and evaluation of multiple videos. Finally, we validated the proposed method on the public datasets NTU-RGB 60 and NTU-RGB 120, and the experimental results demonstrated the effectiveness of the method.http://www.sciencedirect.com/science/article/pii/S1110016824014509Deep learningIoTAction recognitionNeural networkEvaluation system |
| spellingShingle | Wei Qin Junying Meng The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks Alexandria Engineering Journal Deep learning IoT Action recognition Neural network Evaluation system |
| title | The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks |
| title_full | The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks |
| title_fullStr | The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks |
| title_full_unstemmed | The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks |
| title_short | The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks |
| title_sort | research on dance motion quality evaluation based on spatiotemporal convolutional neural networks |
| topic | Deep learning IoT Action recognition Neural network Evaluation system |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824014509 |
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