# Perceiving Humans: from Monocular 3D Localization to Social Distancing

arXiv: https://arxiv.org/abs/2009.00984 · HTML: https://mlsanity.com/paper/2009.00984/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [AirSim360: A Panoramic Simulation Platform within Drone View](https://mlsanity.com/paper/2512.02009/) (2025)
- [Keypoint Communities](https://mlsanity.com/paper/2110.00988/) (2021)
- [From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration](https://mlsanity.com/paper/2212.09298/) (2022)
- [Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm](https://mlsanity.com/paper/2103.06759/) (2021)
- [Monocular Person Localization under Camera Ego-motion](https://mlsanity.com/paper/2503.02916/) (2025)
- [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)

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their body orientation from a single image. We address the challenges related to the ill-posed monocular 3D tasks by proposing a neural network architecture that predicts confidence intervals in contrast to point estimates. Our neural network estimates human 3D body locations and their orientation with a measure of uncertainty. Our proposed solution (i) is privacy-safe, (ii) works with any fixed or moving cameras, and (iii) does not rely on ground plane estimation. We demonstrate the performance of our method with respect to three applications: locating humans in 3D, detecting social interactions, and verifying the compliance of recent safety measures due to the COVID-19 outbreak. We show that it is possible to rethink the concept of "social distancing" as a form of social interaction in contrast to a simple location-based rule. We publicly share the source code towards an open science mission.