HUMAN DETECTION AND SOCIAL DISTANCING MEASUREMENT IN A VIDEO

Abstract

The purpose of this research project is to find the best solution for measuring the distance between people in a video to track the possible COVID-19 social-distancing. This research aims to create a web-application that can be used with closed-circuit televisions (CCTVs) to track positions of persons in interested area and measure distances between any pairs of persons each frame of a video. The process in this project is separated into 3 parts, including 1) tracking positions of people in a video, 2. calibrating camera views, and 3. measuring distances between any two persons. The tracking technique is based on YOLO algorithm, a famous object detection algorithm, that identifies specific objects in the video. In this project, YOLOv3 is used to detect humans to create the bounding box for getting the position in the frame. After getting the bounding box, finding the distance between any pairs in the video is done by using perspective transformation from camera-view into top-down view. Then, the Euclidean distance is used to find the distance of every pair in the video. Any distances closer than 2-meter will be indicated with a line between two people and printed the distance next to the line. The result of perspective transformation is compared with the checkerboard's camera calibration to compare the error rate in several case scenarios.