Monitoring pandemic precautionary protocols using real-time surveillance and artificial intelligence

Abstract

The worst possible situation faced by humanity, COVID-19, is proliferating across more than 180 countries and about 37, 000, 000 confirmed cases, along with 1, 000, 000 deaths worldwide as of October 2020. The absence of any medical and strategic expertise is a colossal problem, and lack of immunity against it increases the risk of being affected by the virus. Since the absence of a vaccine is an issue, social spacing and face covering are primary precautionary methods apt in this situation. This study proposes automation with a deep learning framework for monitoring social distancing using surveillance video footage and face mask detection in public and crowded places as a mandatory rule set for pandemic terms using computer vision. The paper proposes a framework is based on YOLO object detection model to define the background and human beings with bounding boxes and assigned Identifications. In the same framework, a trained module checks for any unmasked individual. The automation will give useful data and understanding for the pandemic's current evaluation; this data will help analyse the individuals who do not follow health protocol norms.