A Comparative Study of Motion Feature Recognition under Different Learning Methods

[1] Laptev I, Marszalek M, Schmid C, et al. Learning realistic human actions from movies[C]//Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2008. Search in Google Scholar

[2] Cheng Suhua. Human motion recognition based on depth information coding [D]. Xi ’an: xidian university, 2017 (in Chinese) Search in Google Scholar

[3] Geng Yining, Liu Shuai Shi, Liu Taiting, Yan Wenyang, Lian Yufeng. Survey of pedestrian detection technology based on computer vision [J]. Computer Application, 2021, 41(S1):43–50. (in Chinese) Search in Google Scholar

[4] Qu Xiao, Wu Naipeng. Observation on the application effect of computer vision in medical field [J]. China Health Industry, 2020, 17(13):168–170. (in Chinese) Search in Google Scholar

[5] Dapeng, Tao, Lianwen, et al. Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition. [J]. IEEE transactions on neural networks and learning systems, 2016. Search in Google Scholar

[6] J. Yang, M. N. Nguyen, P. P. San, X. Li, S. Krishnaswamy. Deep convolutional neural networks on multichannel time series for human activity recognition[C]//2015 24th International Conference on Artificial Intelligence, 2015: 3995–4001. Search in Google Scholar

[7] Liu Juanhong, Hu Mi, Huang Heyu. End-to-end speech recognition based on deep convolution neural network [J]. computer applications and software, 2020, 37(04):192–196. (in Chinese) Search in Google Scholar

[8] Cornacchia M, Ozcan K, Zheng Y, et al. A Survey on Activity Detection and Classification using Wearable Sensors[J]. IEEE Sensors Journal, 2016:1–1. Search in Google Scholar

[9] Chu Jinghui, Luo Wei, Lu Wei. Application of Multi-graph Embedded Representation in Human Motion Pattern Recognition [J]. Computer Science and Exploration, 2017, 11 (6):941–949. (in Chinese) Search in Google Scholar

[10] Jia, Li Yuxiu, Zhao Mingyuan, et al. Line feature correction and purification method for image-to-line feature matching [J]. journal of image and graphics, 2019, 3(7): 1176–1187(in Chinese) Search in Google Scholar

[11] K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos [C]//Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS). New York: ACM, 2014, 1: 568–576. Search in Google Scholar

[12] L.-M. Wang, Y.-J. Xiong, Z. Wang, Y. Qiao, D.-H. Lin, X.-O. Tang, and L. V. Gool. Temporal segment networks: Towards good practices for deep action recognition. [C]//Proceedings of the 2016 European Conference on Computer Vision (ECCV). Berlin: Springer, 2016: 20–36. Search in Google Scholar

[13] Liu Zhe, Zhang Weihua, Li Songbai, Wei Yuzhou. Research on the application of basketball training system based on Kinect and nine-axis sensor [J]. Computer fans, 2017(11):35. (in Chinese) Search in Google Scholar

[14] Guo Rong. Research on EEG feature extraction based on motor imagery [J]. Information Technology and Network Security, 2021, 40(01):62–66. (in Chinese) Search in Google Scholar

[15] Lotte F, Bougrain L, Cichocki A, et al. A review of classification algorithms for EEG – based brain – computer interfaces: a 10 year update[J]. Journal of Neural Engineering, 2018, 15 (3):031005. Search in Google Scholar

[16] Ke Zhang, Feng Xiaohan, Guo Yurong, Su Yukun, TongFu.kabu, Zhao Zhenbing, Ma Zhanyu, Ding Qiaolin. Overview of deep convolution neural network model for image classification [J]. journal of image and graphics, 2021, 26(10):2305–2325. (in Chinese) Search in Google Scholar

[17] Wu Qing, Fu Yanlin. Overview of feature selection methods of support vector machine [J]. Journal of Xi ’an University of Posts and Telecommunications, 2020, 25(05):16–21. (in Chinese) Search in Google Scholar