# COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios

arXiv: https://arxiv.org/abs/2008.06585 · HTML: https://mlsanity.com/paper/2008.06585/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19](https://mlsanity.com/paper/2103.08773/) (2021)
- [Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm](https://mlsanity.com/paper/2103.06759/) (2021)
- [MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and Infrared Sensing](https://mlsanity.com/paper/2110.03979/) (2021)
- [Covy: An AI-powered Robot with a Compound Vision System for Detecting Breaches in Social Distancing](https://mlsanity.com/paper/2207.06847/) (2022)

## Abstract (arXiv)

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.