Real-Time Safety Behavior Detection Technology of Indoors Power Personnel Based on Human Key Points  

作　　者：杨坚 李聪敏 洪道鉴 卢东祁 林秋佳 方兴其 喻谦 张乾 YANG Jian;LI Congmin;HONG Daojian;LU Dongqi;LIN Qiujia;FANG Xingqi;YU Qian;ZHANG Qian(State Grid Zhejiang Taizhou Power Supply Company,Taizhou 318000,Zhejiang,China;Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China;Zhejiang Huayun Information Technology Co.,Ltd.,Hangzhou 310012,China)

机构地区：[1]State Grid Zhejiang Taizhou Power Supply Company,Taizhou 318000,Zhejiang,China [2]Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China [3]Zhejiang Huayun Information Technology Co.,Ltd.,Hangzhou 310012,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2024年第2期309-315,共7页上海交通大学学报（英文版）

基　　金：the Science and Technology Program of State Grid Corporation of China(No.5211TZ1900S6)。

摘　　要：Safety production is of great significance to the development of enterprises and society.Accidents often cause great losses because of the particularity environment of electric power.Therefore,it is important to improve the safety supervision and protection in the electric power environment.In this paper,we simulate the actual electric power operation scenario by monitoring equipment and propose a real-time detection method of illegal actions based on human body key points to ensure safety behavior in real time.In this method,the human body key points in video frames were first extracted by the high-resolution network,and then classified in real time by spatial-temporal graph convolutional network.Experimental results show that this method can effectively detect illegal actions in the simulated scene.

关 键 词：real-time behavior recognition human key points high-resolution network spatial-temporal graph convolutional network 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]