DA-GCN-based coal mine personnel action recognition method

Gong-kuang zidonghua

YUN Xiao1¹  HUANG Han1¹  ZHOU Yu1¹  CHENG Xiaozhou1,2²  SUN Yanjing1,3³ 

[1] 1.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;1.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China； 2.Institute of Mineral Processing and Automation, Sinosteel Maanshan General Institute of Mining Research Co., Ltd., Maanshan 243000, China;1.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China； 3.Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration, China University of Mining and Technology, Xuzhou 221116, China ;

关键词: coal mine video monitoring;    key point extraction;    underground personnel action recognition;    dynamic multi-layer perception graph convolution;    dynamic attention mechanism;   

DOI  :  10.13272/j.issn.1671-251x.17721

来源: DOAJ

【 摘 要 】

At present, the monitoring video in coal mine production area is vague, the type of personnel actions is complex, and the accuracy of conventional action recognition methods is low. In order to solve the above problems, a coal mine personnel action recognition method based on dynamic attention and multi-layer perception graph convolutional network (DA-GCN) is proposed. The Openpose algorithm is used to extract the key points of the human body in the input video to obtain the key point information of the human body in 3 dimensions and 18 coordinates, reducing the interference of fuzzy background information. The spatial characteristics of the key points of the human body is extracted by dynamic multilayer perception graph convolution network (D-GCN), and the temporal characteristics of the key points of the human body is extracted by temporal convolutional network (TCN) so as to improve the generalization ability of the network for different actions. The dynamic attention mechanism is used to enhance the network's attention to action key frames and key skeletons to further mitigate the impact of poor video quality. The softmax classifier is used for action classification. Through scene analysis, underground actions are classified into five types, including standing, walking, sitting, crossing and operating equipment. The method constructs a Cumt-Action data set that applicable to coal mine scenes. The experimental results show that the highest accuracy rate of D-GCN in the Cumt-Action data set is 99.3%, and the highest recall rate is 98.6%. Compared with other algorithms, DA-GCN has higher recognition accuracy in both the Cumt-Action data set and the public data set NTU-RGBD.

【 授权许可】

Unknown