# A Vision-based Social Distancing and Critical Density Detection System for COVID-19

arXiv: https://arxiv.org/abs/2007.03578 · HTML: https://mlsanity.com/paper/2007.03578/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [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)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [LLMCount: Enhancing Stationary mmWave Detection with Multimodal-LLM](https://mlsanity.com/paper/2409.16209/) (2024)
- [Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference](https://mlsanity.com/paper/2204.10539/) (2022)
- [UAV-based Crowd Surveillance in Post COVID-19 Era](https://mlsanity.com/paper/2111.14176/) (2021)
- [Birds Eye View Social Distancing Analysis System](https://mlsanity.com/paper/2112.07159/) (2021)
- [Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs](https://mlsanity.com/paper/2204.10541/) (2022)
- [Monitoring Social-distance in Wide Areas during Pandemics: a Density Map and Segmentation Approach](https://mlsanity.com/paper/2104.03361/) (2021)
- [Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment](https://mlsanity.com/paper/2104.10891/) (2021)
- [Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations](https://mlsanity.com/paper/2112.06428/) (2021)
- [On the Complexity of Object Detection on Real-world Public Transportation Images for Social Distancing Measurement](https://mlsanity.com/paper/2202.06639/) (2022)
- [Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images](https://mlsanity.com/paper/2203.10194/) (2022)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)

## Abstract (arXiv)

Social distancing has been proven as an effective measure against the spread of the infectious COronaVIrus Disease 2019 (COVID-19). However, individuals are not used to tracking the required 6-feet (2-meters) distance between themselves and their surroundings. An active surveillance system capable of detecting distances between individuals and warning them can slow down the spread of the deadly disease. Furthermore, measuring social density in a region of interest (ROI) and modulating inflow can decrease social distancing violation occurrence chance. On the other hand, recording data and labeling individuals who do not follow the measures will breach individuals' rights in free-societies. Here we propose an Artificial Intelligence (AI) based real-time social distancing detection and warning system considering four important ethical factors: (1) the system should never record/cache data, (2) the warnings should not target the individuals, (3) no human supervisor should be in the detection/warning loop, and (4) the code should be open-source and accessible to the public. Against this backdrop, we propose using a monocular camera and deep learning-based real-time object detectors to measure social distancing. If a violation is detected, a non-intrusive audio-visual warning signal is emitted without targeting the individual who breached the social distancing measure. Also, if the social density is over a critical value, the system sends a control signal to modulate inflow into the ROI. We tested the proposed method across real-world datasets to measure its generality and performance. The proposed method is ready for deployment, and our code is open-sourced.