Application of big data technology in traditional dance video movement recognition research

[1] Li, X., Zhai, W., & Cao, Y. (2021). A tri‐attention enhanced graph convolutional network for skeleton‐ based action recognition. IET Computer Vision, 15. Search in Google Scholar

[2] Bai, Y., & Chen, Y. (2021). Human motion analysis and action scoring technology for sports training based on computer vision features. Journal of Intelligent and Fuzzy Systems(34), 1-9. Search in Google Scholar

[3] Castro, D., Hickson, S., Sangkloy, P., Mittal, B., Dai, S., & Hays, J., et al. (2018). Let’s dance: learning from online dance videos. Search in Google Scholar

[4] Wei, Y., Jiao, L., Wang, S., Bie, R., & Liu, D. (2016). Sports motion recognition using mcmr features based on interclass symbolic distance. International Journal of Distributed Sensor Networks, 2016(3), 1-15. Search in Google Scholar

[5] Lelievre, N., St Germain, L., & Ste-Marie, D. M. (2021). Varied speeds of video demonstration do not influence the learning of a dance skill. Human movement science(75-), 75. Search in Google Scholar

[6] Lucía C. Acevedo. (2021). Patrizia veroli and gianfranco vinay (eds), music-dance: sound and motion in contemporary discourse. Dance Research, 39(2), 274-277. Search in Google Scholar

[7] Sun, Y., & Chen, J. (2021). Human movement recognition in dancesport video images based on chaotic system equations. Advances in Mathematical Physics, 2021. Search in Google Scholar

[8] Wang, D., Xie, F., Yang, J., & Liu, Y. (2021). Industry robotic motion and pose recognition method based on camera pose estimation and neural network. International Journal of Advanced Robotic Systems. Search in Google Scholar

[9] Peng, F. T., & Zhang, H. (2021). Research on action recognition method of dance video image based on human-computer interaction. Hindawi Limited. Search in Google Scholar

[10] Li, Z. (2021). Three-dimensional diffusion model in sports dance video human skeleton detection and extraction. Advances in Mathematical Physics, 2021. Search in Google Scholar

[11] Luo, W., & Ning, B. (2022). High-dynamic dance motion recognition method based on video visual analysis. Scientific Programming, 2022, 1-9. Search in Google Scholar

[12] Wu, H. (2021). Design of embedded dance teaching control system based on fpga and motion recognition processing. Microprocessors and Microsystems, 83(3), 103990. Search in Google Scholar

[13] Tsuruta, S., & Hachimura, K. (2014). Real-time motion recognition of complex whole body motion for virtual dance collaboration. International Journal of Digital Content Technology & Its Applic, 23-30. Search in Google Scholar

[14] Zheng, H., Liu, D., & Liu, Y. (2021). Design and research on automatic recognition system of sports dance movement based on computer vision and parallel computing. Microprocessors and microsystems(Feb.), 80. Search in Google Scholar

[15] Qiao, L., & Shen, Q. H. (2021). Human action recognition technology in dance video image. Hindawi Limited. Search in Google Scholar

[16] Gao, Y., & Xu, D. (2021). Application of posture recognition service system based on information fusion smart sensor in dance training. Journal of Sensors. Search in Google Scholar

[17] Tang, J. G. (2021). An action recognition method for volleyball players using deep learning. Scientific programming(Pt.9), 2021. Search in Google Scholar

[18] Hei, H., Jian, X., & Xiao, E. (2021). Sample weights determination based on cosine similarity method as an extension to infrared action recognition. Journal of Intelligent and Fuzzy Systems, 40(9), 1-12. Search in Google Scholar

[19] Wang, H., Yu, B., Xia, K., Li, J., & Zuo, X. (2021). Skeleton edge motion networks for human action recognition. Neurocomputing, 423(10–12), 1-12. Search in Google Scholar

[20] Zhu, F., Shao, L., & Lin, M. (2013). Multi-view action recognition using local similarity random forests and sensor fusion. Pattern Recognition Letters, 34(1), 20-24. Search in Google Scholar

[21] Liu, Nian, Kan, & Jiang-ming. (2016). Improved deep belief networks and multi-feature fusion for leaf identification. Neuruting, 216, 460-467. Search in Google Scholar

[22] Xiong, H., Tang, Y. Y., Murtagh, F., Rutkowski, L., & Berkovsky, S. (2021). A diversified shared latent variable model for efficient image characteristics extraction and modelling. Neurocomputing(Jan.15), 421. Search in Google Scholar

[23] Ratha, S. K., Renuka, N., Abunama, T., Rawat, I., Bux, F., & Kazmerski, L. (2022). Hydrothermal liquefaction of algal feedstocks: the effect of biomass characteristics and extraction solvents. Renewable and Sustainable Energy Reviews, 156. Search in Google Scholar

[24] Wang, X., Zhao, Q., Han, F., Zhang, J., & Jiang, P. (2021). Canopy extraction and height estimation of trees in a shelter forest based on fusion of an airborne multispectral image and photogrammetric point cloud. Journal of Sensors, 2021(3), 1-13. Search in Google Scholar

[25] Li, S., Li, W., Wang, Z., & An, D. (2022). Research and implementation of parallel artificial fish swarm algorithm based on ternary optical computer. Mobile networks & applications. Search in Google Scholar

[26] Isobe, S., Tamura, S., Hayamizu, S., Gotoh, Y., Nose, M., & Chen, M. L. (2021). Multi-angle lipreading with angle classification-based feature extraction and its application to audio-visual speech recognition. Future Internet, 13. Search in Google Scholar