COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios

TL;DR

This paper introduces a robot-based system that automatically detects social distancing violations in crowded indoor environments using commodity sensors, and also monitors individual temperatures wirelessly.

Contribution

The novel method combines RGB-D, lidar, and thermal sensors on a mobile robot to detect social distancing breaches without assumptions on crowd density or movement direction.

Findings

Effective detection of social distancing violations in various scenarios

Integration of thermal imaging for temperature monitoring

Improved accuracy with combined static CCTV and mobile robot sensors

Abstract

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.