Computer-vision-based abnormal human behavior detection and analysis in electric power plant

With the increasing demand for intelligent security in power plants, the rapid and accurate processing of massive surveillance video data is urgently needed. Researches on the detection and analysis of abnormal human behaviors in power plants still focus on traditional image processing technology, and most of them lack robustness. In this article, abnormal behavior detection and analysis system based on personnel information are proposed to solve the above problems. The proposed method using an improved Y oLov3 algorithm first detects persons and extracts abnormal behavior information on this basis. In the implementation, some training tricks are introduced to improve performance. Experimental results show that the system can effectively detect abnormal human behaviors, and the improved Y oLov3 algorithm can also effectively improve model performance. The proposed abnormal behavior detection and analysis system based on personnel information prove its effectiveness through experiments, which can efficiently perform in power plants with lower computing costs.