A Unified Framework for Monitoring Social Distancing and Face Mask Wearing Using Deep Learning: An Approach to Reduce COVID-19 Risk
P. Kaviya, P. Chitra, B. Selvakumar
Abstract
Corona Virus Disease 2019 (COVID-19) is caused by Severe Acute Syndrome Corona Virus 2 (SARS-COV-2). It has become a pandemic disease of the 21st century, killing many lives. During this pandemic situation, precautious measures like social distancing and wearing face mask are being followed globally to break the COVID chain. A pre-programmed viewing system is needed to monitor whether these COVID-19 appropriate behaviours are being followed by the commoners and to ensure COVID-19 preventive measures are followed appropriately. In this work, a deep learning based predictive model and live risk analysis application has been proposed, which detects the high-risk prone areas based on social distancing measures among individuals and face mask wearing tendency of the commoners. The proposed system utilizes ImageNet-1000 dataset for human detection using You Only Look Once (YOLOv3) object detection algorithm; Residual Neural Network (ResNet50v2) uses Kaggle dataset and Real-World Masked Face Dataset (RMFD) for detecting if the persons are face masked or not. Detected human beings (in side-view) are transformed to top view using Top-View Transform Model (TVTM) followed by the calculation of interpersonal distance between the pedestrians and categorized them into three classes include high risk, medium risk, low risk. This unified predictive model provided an accuracy of 97.66%, precision of 97.84%, and F1-Score of 97.92%.