In order to solve a series of disadvantages of slow efficiency and high cost brought by the traditional human action recognition method based on manual feature extraction, in order to prove the feasibility of using graph convolution neural network and action recognition, a real-time dangerous action alarm system based on graph convolution neural network is designed and implemented. The system includes data collection, human posture estimation, action recognition, dangerous action alarm and other functions. The feasibility of using graph convolution neural network to realize real-time dangerous action alarm system is proved by the system. Even if the graph convolution neural network does not pass the training, it can extract good features only by using the original random initialization parameters. If certain annotation information is given, its effect can be greatly improved. After the system adopts the graph convolution neural network, the action recognition can be more accurate, more efficient, faster and more reliable than the traditional method.

Cong Wang, He Zhang, Zhengli Zhai. Real time dangerous action warning system based on graph convolution neural network. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 6: 89-94. https://doi.org/10.25236/AJCIS.2022.050613.

[1] Ye Dian. Research on human behavior recognition technology based on graph convolution neural network. 2021. Guangdong University of technology, MA thesis.

[2] Shotton J, Fitzgibbon A, Cook M, et al. Real-time human pose recognition in parts from single depth images[C].//CVPR 2011. Ieee, 2011: 1297-1304.

[3] LeC un Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

[4] Yuan Yating. Design and implementation of human motion recognition system based on graph convolution neural network [D]. University of Chinese Academy of Sciences (Shenyang Institute of computing technology, Chinese Academy of Sciences), 2021 DOI:10.27587/d.cnki. gksjs. 2021.000037.

[5] Shahroudy A, Liu J, Ng T T, et al. Ntu rgb+ d: A large scale dataset for 3d human activity analysis[C].//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1010-1019.

[6] Donahue J, Anne Hendricks L, Guadarrama S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 2625-2634.

[7] Yan S, Xiong Y, Lin D. Spatial temporal graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1).