According to data obtained by the World Health Organization, the global pandemic of COVID-19 has severely impacted the world and has now infected more than eight million people worldwide. 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 create safe environment that contributes to public safety, we propose an efficient computer vision-based approach 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 as a webapp to monitor activity and detect violations through camera. After detection of breach, the webapp can send alert signal to control centre at state police headquarters and also give alarm to public. In this proposed system modern deep learning algorithm have been mixed with geometric techniques for building a robust modal which covers three aspects of detection, tracking, and validation. Thus, the proposed system favours the society by saving time and helps in lowering the spread of corona virus. It can be implemented 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 also can be used in any place.

This project relies on a combination of IoT with ML. Its scope also depends on computer networking, website designing, artificial intelligence, and problem-solving. It uses knowledge in neural networks learned in artificial intelligence, server connections and terminologies learn in computer networks and systems, database monitoring, and sensor testing in electronic system design. The technology used in this system covers every part of the electronics and computer curriculum.