Automated Inspection of Face Mask and Social Distancing Using Artificial Intelligence

According to the data obtained by the World Health Organization, COVID-19, and the global pandemic has severely impacted the world and infected more than hundreds of million people worldwide which includes more than three million deaths. This global pandemic enforced governments across the world to impose lockdowns in order to prevent the virus transmissions. Reports indicate that wearing face masks and following safe social distancing are two of the enhanced safety protocols need to be followed in public places in order to prevent the spread of the virus. To ensure the public safety in environment, we propose an efficient Computer Vision based approach that focused on the real-time automated monitoring of people to detect both safe social distancing and face masks in public places by implementing the model to monitor the activity and detect violations through camera. After detection of breach, the system sends to control center at state police headquarters and also give alarm to public. In this proposed system, modern deep neural network based model have been mixed with geometric techniques for building a robust model which covers three aspects of detection, tracking, and validation. Thus, the proposed system helps the society in saving time and reducing the spread of corona virus. It could be practiced effectively in current situation when lockdown is eased to inspect persons in public gatherings, shopping malls, etc. Automated inspection reduces manpower to inspect the public and can be used in any place to ensure safety.