# Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm

arXiv: https://arxiv.org/abs/2103.06759 · HTML: https://mlsanity.com/paper/2103.06759/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 61 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 19 of 19)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [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)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://mlsanity.com/paper/1704.07809/) (2017)
- [Deep Cosine Metric Learning for Person Re-Identification](https://mlsanity.com/paper/1812.00442/) (2018)
- [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)
- [The Visual Social Distancing Problem](https://mlsanity.com/paper/2005.04813/) (2020)
- [Single Image Human Proxemics Estimation for Visual Social Distancing](https://mlsanity.com/paper/2011.02018/) (2020)
- [Perceiving Humans: from Monocular 3D Localization to Social Distancing](https://mlsanity.com/paper/2009.00984/) (2020)
- [COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios](https://mlsanity.com/paper/2008.06585/) (2020)
- [Inter-Homines: Distance-Based Risk Estimation for Human Safety](https://mlsanity.com/paper/2007.10243/) (2020)

## Built on by (most cited first, 3 of 3)

- [Automatic Main Character Recognition for Photographic Studies](https://mlsanity.com/paper/2106.09064/) (2021)
- [Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives](https://mlsanity.com/paper/2204.02149/) (2022)
- [Estimating Distances Between People using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight](https://mlsanity.com/paper/2303.11520/) (2023)

## Abstract (arXiv)

The COVID-19 virus has caused a global pandemic since March 2020. The World Health Organization (WHO) has provided guidelines on how to reduce the spread of the virus and one of the most important measures is social distancing. Maintaining a minimum of one meter distance from other people is strongly suggested to reduce the risk of infection. This has created a strong interest in monitoring the social distances either as a safety measure or to study how the measures have affected human behavior and country-wise differences in this. The need for automatic social distance estimation algorithms is evident, but there is no suitable test benchmark for such algorithms. Collecting images with measured ground-truth pair-wise distances between all the people using different camera settings is cumbersome. Furthermore, performance evaluation for social distance estimation algorithms is not straightforward and there is no widely accepted evaluation protocol. In this paper, we provide a dataset of varying images with measured pair-wise social distances under different camera positionings and focal length values. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate social distance estimation algorithms. We also propose a method for automatic social distance estimation. Our method takes advantage of object detection and human pose estimation. It can be applied on any single image as long as focal length and sensor size information are known. The results on our benchmark are encouraging with 92% human detection rate and only 28.9% average error in distance estimation among the detected people.