Real time social distance detection using Deep Learning
Shailaja Salagrama, H. Hemant Kumar, R. Nikitha, G Vishal Prasanna, Kanhaiya Sharma, Shashank Awasthi
Abstract
In the Covid-19 pandemic, if residents do not take action to prevent the virus from spreading, the process of softening the curve of the coronavirus will be complicated in the face of the worldwide Covid-19 scenario. Without vaccination, the only method to combat the disease is social isolation. The proposed system employs the You Only Look Once, Version 3 (YOLOv3) object detection model to identify persons in the background and bind boxes around them, and assign IDs for in-depth tracking of recognized people. This study focuses on public space surveillance and determining whether or not people maintain social distance as per Covid-19 guidelines. YOLOv3 is an efficient tracking method that produces positive results with a moderate mean Average Precision(MAP) and Frame Per Second (FPS) score for monitoring community deviations in real-time. In this study, YOLOv3 is used for object capture, and the OpenCV library is used for image processing. Proposed work is helpful in areas where big crowds are expected, such as retail malls, movie theatres, railway stations, airports, and public places.