Design of Abnormal Behavior Detection System in the State Grid Business Office

Nowadays, with the popularization of electricity, the status of grid business office in people’s lives has become more and more important. Grid business office often have a large flow of people, so it becomes very necessary for abnormal detection of grid business offices. Traditional video surveillance has a lot of problems. This paper uses various computer vision technologies to improve traditional video surveillance. First, the YOLO v3 algorithm is introduced to detect the number of people in the business office, and then the motion foreground extraction algorithm is used to calculate the contour of the human body. The contour judges whether there is an abnormal situation currently, and finally introduces the concept of image entropy to judge whether there is intense movement currently. Finally, in the end of the paper, the current work is summarized and the future word is prospected.