Vision-based approach for automated social distance violators detection

Abstract

Social distancing is a necessary precaution measure taken in order to have more control over the outbreak of infectious diseases such as COVID-19. Most of Social distancing monitoring approaches are based on Bluetooth and mobile phones that require an app to be downloaded on all phones. This paper proposes a different approach to monitor social distancing, using cameras, and combining different computer vision algorithms. The approach utilizes the concept of inverse perspective mapping (IPM) together with the camera's intrinsic information to produce a bird's eye view with real-world coordinates of the frame being processed from a video source. The process starts with image enhancement, foreground detection using Gaussian Mixture Model (GMM) background subtraction, tracking using Kalman filter, computing real-world distance measurements between individuals, and detecting those who have been in less than 2 meters apart as they are considered to be in contact. This tool could assist the efforts of the governments to contain the virus. It can be implemented in closed areas or institutions, monitor the extent of people's commitment, and provide analysis and a faster approach to detect possibly corona suspicion cases. The approach is tested on the task decomposition data set, which included frames of closed areas and the camera's intrinsic parameters. Another data set was created with different scenarios to increase the confidence level of our algorithm. The results showed the success of our approach in detecting the violation in social distancing with accurate measures of the real-world coordinates.