Research on motion recognition method in college dance teaching based on deep learning

Qianwen Zhao; Mingde Fan

doi:10.1117/12.3003933

11 October 2023 Research on motion recognition method in college dance teaching based on deep learning

Qianwen Zhao, Mingde Fan

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128002T (2023) https://doi.org/10.1117/12.3003933
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

This article proposes a deep learning method for recognizing dance action videos. In this study, a dual path convolutional neural network is proposed to improve the traditional machine learning method. This algorithm can extract pixel information from video images, as well as the differential information of video actions. The Golden Tower algorithm is used to extract the changes in the lattice over time as the change features of convolutional neural networks. This study takes dance action recognition as an example and tests a given dataset. A comparison was made between the existing algorithm Inception V3 network and 3D-CNN network. The test results show that compared with the Inception V3 network, the algorithm proposed in this paper improves F1 by 10.90%, and improves recognition accuracy by 10.85% and 5.27%, respectively.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qianwen Zhao and Mingde Fan "Research on motion recognition method in college dance teaching based on deep learning", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128002T (11 October 2023); https://doi.org/10.1117/12.3003933

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