Face Mask Detection and Social Distancing Monitoring
Reena Pagare · International Journal for Research in Applied Science and Engineering Technology · 2021
During pandemic COVID-19, the World Health Organization (WHO) reports suggest that the two main routes of transmission of the COVID-19 virus are respiratory droplets and physical contact. Respiratory droplets are generated when an infected person coughs or sneezes. Any person in close contact (within 1 m) with someone who has respiratory symptoms (coughing, sneezing) is at risk of being exposed to potentially infective respiratory droplets. Droplets may also land on surfaces where the virus could remain viable; thus, the immediate environment of an infected individual can serve as a source of transmission (contact transmission). Wearing a medical mask and social distancing is one of the prevention measures that can limit the spread of certain respiratory viral diseases, including COVID-19. World Health Organization has made wearing masks and also social distancing compulsory to protect against this deadly virus.This paper is about we developed a generic Deep Neural Network-Based model for mask detection, tracking using cameras and also we developed second application for social distancing monitoring application with the help of Python and Computer Vision.