Accurate and Contactless COVID-19 Fever Screening and Attendance Tracking System

Over the past two years, COVID-19 has led to and is still leading to lots of deaths to date. Many industries have been affected by that, and governments have united in finding ways to mitigate the spread of the disease, thus leading life to return to normal. There are several ways that were followed to do that, such as social distancing, thermal screening, and virtual communication. Thermal screening has proven its practicality in certain entities that require face-to-face contact. Researchers have been contributing to finding effective ways to develop screening methods to help re-accelerate the learning process. This paper proposes a fever screening system to record and track individuals' temperature and an attendance tracking system for educational institutions. The system measures the individual's temperature and records it, and saves their attendance in a database. After completing the measurement taking of an individual, the system uses a buzzer to inform the following individual that it is their turn. This allows the institution to monitor any temperature spikes among the individuals while recording their attendance without close contact at the entrance. Our results validate the usefulness and potential of our system as a fever screening and attendance tracking tool. It also opens the door for further development, allowing regular operation in educational institutions during any upcoming pandemics. © 2022 IEEE.

Vision-based Approach for Automated Social Distance Violators Detection

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. © 2020 IEEE.