Social distance measurement for indoor environments

Abstract

Social distancing is a suggested solution by many scientists, health care providers and researchers to reduce the spread of COVID-19 in public places. Over a year ago most countries have closed their borders, put people under lockdown, and have been suspending people from work and travel. However, there are still many organizations that need to operate, especially hospitals, services industry, governments, etc. However, people cannot maintain social distancing which includes staying at least 1.5 2 meters from other people because they need to communicate with each other. As a result, this increases the infection of Covid-19. This work proposes a social distancing tracking tool in offices or indoor places. We propose a YOLOv5-based Deep Neural Network (DNN) model to automate the process of monitoring the social distancing via object detection and tracking approaches. We detect office objects of known size and use it to estimate the social distance in real-time with the bounding boxes in indoor environments.