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# Monoloco library [](https://pepy.tech/project/monoloco)
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# Monoloco library [](https://pepy.tech/project/monoloco)
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<img src="docs/monoloco.gif" alt="gif" />
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<img src="docs/monoloco.gif" alt="gif" />
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This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing.
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This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing. Check the [demo video](https://www.youtube.com/watch?v=O5zhzi8mwJ4)!
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> __MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization__<br />
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> __MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization__<br />
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> _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com),
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> _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com),
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[T. Mordan](https://people.epfl.ch/taylor.mordan/?lang=en), [A. Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, ICRA 2021 <br />
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[T. Mordan](https://people.epfl.ch/taylor.mordan/?lang=en), [A. Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, ICRA 2021 <br />
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__[Article](https://arxiv.org/abs/2008.10913)__ __[Citation](#Citation)__ __[Video](#Todo)__
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__[Article](https://arxiv.org/abs/2008.10913)__ __[Citation](#Citation)__ __[Video](https://www.youtube.com/watch?v=pGssROjckHU)__
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<img src="docs/out_000840_multi.jpg" width="700"/>
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<img src="docs/out_000840_multi.jpg" width="700"/>
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@ -188,7 +188,7 @@ The network estimates orientation and box dimensions as well. Results are saved
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<br />
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<br />
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## Training
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## Training
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We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [heere](https://drive.google.com/file/d/1e-wXTO460ip_Je2NdXojxrOrJ-Oirlgh/view?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
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We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [here](https://drive.google.com/file/d/1bJPyA1HuX9uyJYf1IhiDqzhkvSokd4l0/view?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
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Results for MonoLoco++ are obtained with:
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Results for MonoLoco++ are obtained with:
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@ -321,8 +321,6 @@ python -m monoloco.run eval \
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--save \
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--save \
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````
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````
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<img src="docs/results_monoloco_pp." width="550"/>
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By changing the net and the model, the same command evaluates MonStereo model.
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<img src="docs/results_stereo.jpg" width="550"/>
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<img src="docs/results_stereo.jpg" width="550"/>
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@ -355,7 +353,7 @@ When using this library in your research, we will be happy if you cite us!
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@InProceedings{bertoni_2021_icra,
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@InProceedings{bertoni_2021_icra,
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author = {Bertoni, Lorenzo and Kreiss, Sven and Mordan, Taylor and Alahi, Alexandre},
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author = {Bertoni, Lorenzo and Kreiss, Sven and Mordan, Taylor and Alahi, Alexandre},
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title = {MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization},
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title = {MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization},
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booktitle = {International Conference on Robotics and Automation (ICRA)},
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booktitle = {the International Conference on Robotics and Automation (ICRA)},
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year = {2021}
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year = {2021}
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}
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}
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```
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```
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@ -372,7 +370,7 @@ When using this library in your research, we will be happy if you cite us!
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@InProceedings{bertoni_2019_iccv,
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@InProceedings{bertoni_2019_iccv,
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author = {Bertoni, Lorenzo and Kreiss, Sven and Alahi, Alexandre},
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author = {Bertoni, Lorenzo and Kreiss, Sven and Alahi, Alexandre},
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title = {MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation},
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title = {MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation},
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booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
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booktitle = {the IEEE International Conference on Computer Vision (ICCV)},
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month = {October},
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month = {October},
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year = {2019}
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year = {2019}
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}
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}
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