Abstract:As the world recovers from the Covid-19 crisis, major steps are taken worldwide to boost the recovery process. The world is becoming more resilient as a result of vaccination campaigns. In such circumstances, we as citizens must ensure that we adhere to security protocols and norms established by the government. Social distancing and wearing a face mask are the rudimentary elements of this system. The proposed system makes a systematic effort to comply to this. The system keeps track of every person who enters and exits the area under surveillance. In addition to this, a person with high body temperature is blacklisted. This data is entered into a database, and daily logs are maintained. A headcount of people in the area is maintained and admits to the area are given accordingly. If any violations take place, alerts are issued and sent directly to the primary android device. The face mask detection model is trained on a comprehensive real-world dataset. The model uses Convolutional Neural Network (CNN). It will function by recognizing facial boundaries and predicting whether or not you are wearing a face mask in real time. YOLO Object detection algorithm is used to identify people and calculate the euclidean distance between them. This distance is used to keep track of social distancing. A heat map can be generated which later can be referred to sanitize the crowded locations..
Index Terms—Covid-19, Social distancing, Convolutional Neu- ral Network , YOLO, Face Mask Detection.
| DOI: 10.17148/IJARCCE.2022.115163