A Conceptual Framework for Smart Social Distancing for Educational Institutes

The spread of the novel corona virus (COVID-19) has caused society to suffer to a great extent and have affected Governments and health authorities globally. COVID-19 spreads in a very fast manner due to absence of any active therapeutic agents and the lack of immunity against it. Although developed countries have started vaccination against COVID-19;however, availability of vaccines in developing countries is still an issue. Social distancing needs to be maintained in countries where vaccination is started till everyone gets vaccinated and COVID-19 is completely eradicated. To maintain social distancing, permanent lockdowns and curfews are no longer a sustainable solution due to adverse effects on economy. Therefore, there is a dire need to maintain social distancing in educational institutions. Violations of social distancing can be monitored and reported with the help of object detection and tracking techniques so that appropriate actions can be taken to prevent the minimize social distancing. We present comparison between state-of-the-art existing object detection methods and propose a conceptual framework to detect social distancing violations in higher educational institutes. Proposed framework takes live video streams as input, tracks human, and detects masks and violation of social distancing with the help of deep learning method. Our method can be implemented to monitor and reduce the effect of social distancing in educational institutions in real time environment with highest accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.