Abstract
Hand movement recognition is one of hot research topics in the field of computer vision, which has received extensive research interests. However, current classical hand dance movement recognition has high computational complexity and low accuracy. To address these problems, we present a classical hand dance movement recognition and analysis method based on deep learning. Firstly, our method extracts the key frames from the input classical hand dance video by using an inter frame difference method. Secondly, we use a method based on stacked hourglass network to estimate the 2D hand poses of key frames. Thirdly, a network named HandLinearNet with spatial and channel attention mechanisms is constructed for 3D hand pose estimation. Finally, our method uses ConvLSTM for classical hand dance movement recognition, and outputs corresponding classical hand dance movements. The method can recognize 12 basic classical hand dance movements, where users can better analyze and study classical hand dance.
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Acknowledgements
This work was supported by the Funding Project of Beijing Social Science Foundation (No. 19YTC043).
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Cai, X., Lu, Q., Li, F., Liu, S., Hu, Y. (2024). Hand Movement Recognition and Analysis Based on Deep Learning in Classical Hand Dance Videos. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_5
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DOI: https://doi.org/10.1007/978-3-031-50075-6_5
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