Academic Journal of Computing & Information Science, 2022, 5(6); doi: 10.25236/AJCIS.2022.050613.
Cong Wang, He Zhang, Zhengli Zhai
School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, Shandong, China
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.
graph convolution neural network; Action recognition; Dangerous action alarm
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.
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