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# Perceiving Humans in 3D
This repository contains the code for three research projects:
This repository contains the code for two research projects:
1. **MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization**
[README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonStereo.md) & [Article](https://arxiv.org/abs/2008.10913)
![monstereo](docs/out_005523.png)
2. **Perceiving Humans: from Monocular 3D Localization to Social Distancing**
[README](https://github.com/vita-epfl/monstereo/tree/master/docs/SocialDistancing.md) & [Article](https://arxiv.org/abs/2009.00984)
2. **Perceiving Humans: from Monocular 3D Localization to Social Distancing (MonoLoco++)**
[README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonoLoco_pp.md) & [Article](https://arxiv.org/abs/2009.00984)
![social distancing](docs/pull_sd.png)
3. **MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation** (Improved!)
[README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonoLoco.md) & [Article](https://arxiv.org/abs/1906.06059) & [Original Repo](https://github.com/vita-epfl/monoloco)
![monoloco](docs/truck.png)
Both projects has been built upon [Openpifpaf](https://github.com/vita-epfl/openpifpaf)
for 2D pose estimation and [MonoLoco](https://github.com/vita-epfl/monoloco) for monocular 3D localization.
All projects share the AGPL Licence.

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### Work in Progress
For the moment please refer to the [original repository](https://github.com/vita-epfl/monoloco)
```
@InProceedings{bertoni_perceiving,
author = {Bertoni, Lorenzo and Kreiss, Sven and Alahi, Alexandre},
title = {Perceiving Humans: from Monocular 3D Localization to Social Distancing},
booktitle = {arXiv:2009.00984},
month = {September},
year = {2020}
}
```

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# Perceiving Humans: from Monocular 3D Localization to Social Distancing
> 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 deep learning method
that predicts confidence intervals in contrast to point estimates. Our neural network architecture estimates
humans 3D body locations and their orientation with a measure of uncertainty.
Our vision-based system (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.
Indeed, we show that we can 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.

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# Work in progress