# Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment

arXiv: https://arxiv.org/abs/2104.10891 · HTML: https://mlsanity.com/paper/2104.10891/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Features for Multi-Target Multi-Camera Tracking and Re-Identification](https://mlsanity.com/paper/1803.10859/) (2018)
- [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020)
- [Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques](https://mlsanity.com/paper/2005.01385/) (2020)
- [A Vision-based Social Distancing and Critical Density Detection System for COVID-19](https://mlsanity.com/paper/2007.03578/) (2020)
- [Human Pose Estimation for Real-World Crowded Scenarios](https://mlsanity.com/paper/1907.06922/) (2019)
- [Generalizable Pedestrian Detection: The Elephant In The Room](https://mlsanity.com/paper/2003.08799/) (2020)
- [The Visual Social Distancing Problem](https://mlsanity.com/paper/2005.04813/) (2020)
- [Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner](https://mlsanity.com/paper/2002.05534/) (2020)
- [Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World](https://mlsanity.com/paper/2005.05287/) (2020)

## Abstract (arXiv)

Social distancing has been suggested as one of the most effective measures to break the chain of viral transmission in the current COVID-19 pandemic. We herein describe a computer vision-based AI-assisted solution to aid compliance with social distancing norms. The solution consists of modules to detect and track people and to identify distance violations. It provides the flexibility to choose between a tool-based mode or an automated mode of camera calibration, making the latter suitable for large-scale deployments. In this paper, we discuss different metrics to assess the risk associated with social distancing violations and how we can differentiate between transient or persistent violations. Our proposed solution performs satisfactorily under different test scenarios, processes video feed at real-time speed as well as addresses data privacy regulations by blurring faces of detected people, making it ideal for deployments.