# Estimating Distances Between People using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight

arXiv: https://arxiv.org/abs/2303.11520 · HTML: https://mlsanity.com/paper/2303.11520/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images](https://mlsanity.com/paper/2005.11623/) (2020)
- [Single Image Human Proxemics Estimation for Visual Social Distancing](https://mlsanity.com/paper/2011.02018/) (2020)
- [SD-Measure: A Social Distancing Detector](https://mlsanity.com/paper/2011.02365/) (2020)
- [Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm](https://mlsanity.com/paper/2103.06759/) (2021)

## Built on by (most cited first, 1 of 1)

- [Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey](https://mlsanity.com/paper/2304.08193/) (2023)

## Abstract (arXiv)

Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics. A natural resource to accomplish this are surveillance cameras. Unlike previous distance estimation methods, we use a single, overhead, fisheye camera with wide area coverage and propose two approaches. One method leverages a geometric model of the fisheye lens, whereas the other method uses a neural network to predict the 3D-world distance from people-locations in a fisheye image. To evaluate our algorithms, we collected a first-of-its-kind dataset using single fisheye camera, that comprises a wide range of distances between people (1-58 ft) and will be made publicly available. The algorithms achieve 1-2 ft distance error and over 95% accuracy in detecting social-distance violations.