Monitoring Social Distancing Using OpenCv

Abstract

The paper proposes a method for social separating identification based on deep understanding of how to measure the gap between people in order to mitigate the impact of the COVID-19 pandemic. By evaluating with the aid of videos as feedback, the position instrument was developed to make people aware of the importance of keeping a safe distance from one another. The input video outline from the camera has been used as details, along with a free and open source object location system based on YOLOv3. Calculation that was used to determine walker recognition. After that, the input frame outline was modified to elevated perspective for distance estimation in the 2-Dimensional plane. The RED edge and line represent the range between individuals being measured and a part of the rebellious pairing of individuals during the showcase. The proposed strategy is accepted using a pre-recorded feedback frame of people walking around the city on foot. This result demonstrates how the presented methodology can make decisions about social removing estimates for a large number of people in the input picture. As the discovery apparatus was gradually introduced, this developed technique evolved as well