Physical Distancing Monitoring Application with Zone-Based Calibration through YOLOv4 Model

Abstract

Physical Distancing is one of the minimum health protocols where two persons should be at least 1.5 meters apart to lessen the risk of transmission of COVID-19. The study aims to design a real-time monitoring system that detects violations on physical Distancing by applying the You Only Look Once version 4 computer vision model. The program detects the pairwise distance between two persons in a frame and indicates whether they comply with the minimum 1.5 distance between persons. The video frame comprises zone 1 being the farthest from the camera, zone 2, and zone 3 being the nearest from the camera. The program calculates the Euclidean distance between persons and generates a pixel value converted to a metric value by a scale multiplier. The scaling multiplier varies depending on the zone at which the location of the detected person is. The mean absolute error of the distance predicted by the program is at 7.8 centimeters, 5.73 centimeters, and 5.21 centimeters at zones 1, 2, and 3, respectively. The physical distancing detector achieved 95.84% accuracy and 97.08% precision upon evaluating through the confusion matrix.