Social Distance Detection using Customized YOLOv4 (SDDYv4) Model

Abstract

This work propose a technique to detect social distancing using deep learning to ensure the social distance between people, in order to reduce the impact of the coronavirus pandemic. By analysing a video stream, the social distance detecting tool is developed to inform people to keep a safe distance from each other. In this work we have used customized YOLOv4 model which was created by using some layers of YOLO from the initial model and then we trained that layers on a labelled person image dataset so that it can be used only for person detection. Input is given either in the form of video or can be taken from webcam which is then converted to a 2-D frames for measuring distance between two person. Any person not following social distancing would be displayed in red bounding box while those who are following will be displayed in green bounding box. A pre-recorded video was used to test the suggested strategy.We have named our work as SDDYv4.