COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios
This addresses public health monitoring during pandemics for crowded indoor environments, but it is incremental as it builds on existing sensor and robotics technologies.
The paper tackles the problem of monitoring social distancing in crowded areas to reduce COVID-19 transmission by developing a mobile robot system that detects individuals not adhering to distance constraints, achieving performance improvements in various indoor scenarios.
Maintaining social distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not adhering to the social distance constraint, i.e. about 6 feet of space between them. Our approach makes no assumption about the crowd density or pedestrian walking directions. We use a mobile robot with commodity sensors, namely an RGB-D camera and a 2-D lidar to perform collision-free navigation in a crowd and estimate the distance between all detected individuals in the camera's field of view. In addition, we also equip the robot with a thermal camera that wirelessly transmits thermal images to a security/healthcare personnel who monitors if any individual exhibits a higher than normal temperature. In indoor scenarios, our mobile robot can also be combined with static mounted CCTV cameras to further improve the performance in terms of number of social distancing breaches detected, accurately pursuing walking pedestrians etc. We highlight the performance benefits of our approach in different static and dynamic indoor scenarios.