Abstract
The COVID-19 epidemic affects humans irrespective of race, religion,
standing, and caste. It has affected more than 20 million people worldwide. Wearing
face masks and taking public safety measures are two advanced safety measures that
need to be taken in open areas to prevent the spread of the disease. To create a secure
environment that contributes to public safety, we propose a computer-based method
that focuses on automatic real-time surveillance to identify safe general distance and
face masks in public places using a model to monitor movement and detect camera
violations. We achieve 97.6% specificity with the help of OpenCV and MobileNetV2
strategies.
Keywords: Coronavirus, Covid-19, Deep learning, Face-mask-detection, MobileNetV2, OpenCV, Safe-distancing, Transfer learning, YOLO-V3.
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