COVID-19 Monitoring System using Social Distancing and Face Mask Detection on Surveillance video datasets

The new Coronavirus (COVID-19) has affected the world seriously. According to estimates from the World Health Organization (WHO), the worldwide number of novel coronaviruses already has passed 60 million+ and 1,410,378+ fatalities. The compulsory face mask restrictions are increasingly standard in public environments across the world to limit the spread of the disease.

Additionally, many public service providers, educational institutions, offices etc., made a mandatory rule to wear face-masks. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic.

Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance and that the main goal of our method is to detect the presence of face-masks and updating the details of the person into spreadsheet in case of no face mask.

This work is developed using YOLOv2 Algorithm for detection of face mask and Convolutional Neural Networks (CNN) for classifying the person.

 Keywords: COVID-19, Masked-face detection, Face-mask classification, Face-mask recognition, COVID-19 compliant mask detection.