System for Detecting Social Distance During COVID-19 using YOLOv3 and OpenCV

Abstract

A virus called COVID-19 spreads between people in close proximity via minute droplets created through talking, sneezing, coughing, and most commonly by inhalation. Many people have died as a result of the pandemic’s severe respiratory infection, which is still present today. You can reduce your risk of contracting COVID-19 by avoiding physical contact with others. This study suggests a real-time AI framework for people detection, monitoring social distance violations, and categorising people’s social distances based on live video feeds. In this study, YOLOv3 was suggested for object detection. Its straightforward neural network architecture makes it appropriate for embedded devices that are reasonably priced. Comparing the suggested model to other real-time detection methods, it is a better choice. Additionally, with the aid of OpenCV, an open-source toolkit for computer vision, machine learning, and image processing. The major purpose of the image processing feature is to enhance the image quality so that the AI detection system would accurately recognise human movement. Computer vision is used to analyse photos and videos.The final iteration of the prototype algorithm has been put to use in low-cost CCTV Cameras made up of fixed cameras that are placed in any public area where large crowds used to congregate. The suggested method is appropriate for a surveillance system in sustainable smart cities for people detection, social distance classification, and tracking social dis- tance violations. This will make it easier for the government to understand how people who are socially isolated are doing.