An Integrated Approach for Monitoring Social Distancing and Face Mask Detection Using Stacked ResNet-50 and YOLOv5
Inderpreet Singh Walia, Deepika Kumar, Kaushal Sharma, D. Jude Hemanth, Daniela Elena Popescu
Abstract
SARS-CoV-19 is one of the deadliest pandemics the world has witnessed, taking around 5,049,374 lives till now across worldwide and 459,873 in India. To limit its spread numerous countries have issued many safety measures. Though vaccines are available now, still face mask detection and maintain social distance are the key aspects to prevent from this pandemic. Therefore, authors have proposed a real-time surveillance system that would take the input video feed and check whether the people detected in the video are wearing a mask, this research further monitors the humans for social distancing norms. The proposed methodology involves taking input from a CCTV feed and detecting humans in the frame, using YOLOv5. These detected faces are then processed using Stacked ResNet-50 for classification whether the person is wearing a mask or not, meanwhile, DBSCAN has been used to detect proximities within the persons detected.