Machine Learning-Based Social Distance Detection: An Approach Using OpenCV and YOLO Framework

In the fight against coronavirus, social distance has proven to be a very effective tool. To minimize the risk of the virus spreading through physical contact or proximity, the public is being advised to limit their contact with one another. It has previously been demonstrated that deep learning can solve a variety of issues. In our proposed system, we utilize Python, image analysis, and other learning techniques to monitor social distance in public areas and offices to corroborate the social distancing protocol. By analysing live video feeds from cameras, this tool will track if people remain within a safe distance from each other. With this tool, it is possible to predict people at malls, company offices, and stores to see if they are at an appropriate distance from one another.