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. S ince 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.
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