sis 18: e13

Research Article

Residual network based on convolution attention model and feature fusion for dance motion recognition

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  • @ARTICLE{10.4108/eai.16-12-2021.172434,
        author={Dianhuai Shen and Xueying Jiang and Lin Teng},
        title={Residual network based on convolution attention model and feature fusion for dance motion recognition},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={12},
        keywords={dance motion recognition, residual network, convolution attention model, future fusion},
        doi={10.4108/eai.16-12-2021.172434}
    }
    
  • Dianhuai Shen
    Xueying Jiang
    Lin Teng
    Year: 2021
    Residual network based on convolution attention model and feature fusion for dance motion recognition
    SIS
    EAI
    DOI: 10.4108/eai.16-12-2021.172434
Dianhuai Shen1, Xueying Jiang2, Lin Teng3,*
  • 1: College of Music and Dance, Huaqiao University, Xiamen 361000 Fujian, China
  • 2: School of Public Policy and Management, Tsinghua University, Beijing 100000 China
  • 3: Software College, Shenyang Normal University, Shenyang 110034 China
*Contact email: 910675024@qq.com

Abstract

Traditional posture recognition methods have the problems of low accuracy. Therefore, we propose a residual network based on convolution attention model and future fusion for dance motion recognition. Firstly, the fusion features of the relative position, angle and limb length ratio of human body are selected by combining the information of bone key points. The shallow features of the original dance image are extracted and compressed by convolution layer and pooling layer. Then it uses the stacked residual to learn deep features, the gradient dispersion and network degradation can be alleviated. The convolutional attention module is used to assign weighted values to the deep degradation features of the dance. Finally, dance motion detection in complex dance scenes can be realized. The dance movement recognition method proposed in this paper can accurately identify dance motion. Compared with other recognition algorithms, this new algorithm has the best recognition accuracy and faster recognition efficiency.