Enhancing Public Safety: A Real-time Social Distance Monitoring with Computer Vision and Deep Learning

Abstract

In spite of the fact that the COVID-19 epidemic has lately afflicted millions of individuals all over the world, the number of people who are being affected is continuing to climb. In response to the ongoing pandemic scenario throughout the world and in an effort to stop the virus from further disseminating, a number of governments have initiated a number of groundbreaking preventative measures. One of the most effective methods for warding off the spread of infectious diseases is maintaining adequate social distance. In the context of a real-time top view environment, the purpose of this study survey is to propose the use of a social distance framework that is built on deep learning architecture as a preventative strategy for maintaining, monitoring, managing, and lowering the amount of physical connection that occurs between individuals. In order to identify people in the photographs, we made use of a number of different deep learning detection models, including R-CNN, Fast R-CNN, Faster-RCNN, YOLO, and SSD. Because of the significant differences between the top and bottom views of a human's appearance, the architecture was trained using the top view human data set. After that, the Euclidean distance is utilised to derive a pair-wise distance estimate between the individuals depicted in a picture. Using the information obtained from a detected bounding box, one may determine where the centre point of a single detected bounding box is located. A violation threshold is constructed, which is determined by the information of a person's distance to a pixel and determines whether or not two people are in breach of social distance.