Dynamic Surveillance and Implementation of COVID-19 Social Distancing Measures using Advanced Image Processing and R-CNN

Abstract

The recent COVID-19 virus, known as Coronavirus, is a highly contagious illness transmitted through droplets released when an individual who is infected coughs, sneezes, or exhales. Thus, adhering to social distancing guidelines is crucial to curbing the spread of the virus. Governments worldwide have mandated the practice of maintaining safe distances in public areas and supermarkets as part of their efforts to address the global health crisis caused by the COVID-19 pandemic. Observing social distancing measures in both public and private spaces has proven to be a potent preventive measure. The outbreak of COVID-19 has prompted governments across the globe to impose lockdowns in order to mitigate the transmission of the virus. Reports from surveys highlight that maintaining social distance in public settings significantly reduces the risk of viral transmission. Amidst the serious global threats posed by the COVID-19 pandemic, it serves as a stark reminder of the imperative to take precautionary measures to control the spread of the virus. Deep Learning technology has showcased its prowess in image recognition and classification tasks. An image processing algorithm has been employed to identify human faces and implement social distancing protocols. It involves integrating a social distancing system using the technology. The information about social distancing compliance is collected through the model and hosted on secure private websites.