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Human Pose estimation with TensorFlow framework
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# Human Pose Estimation with TensorFlow
Here you can find the implementation of the Human Body Pose Estimation algorithm,
presented in the [DeeperCut](http://arxiv.org/abs/1605.03170) and [ArtTrack](http://arxiv.org/abs/1612.01465) papers:
**Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka and Bernt Schiele
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model.
In _European Conference on Computer Vision (ECCV)_, 2016**
**Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin, Siyu Tang, Evgeny Levinkov, Bjoern Andres and Bernt Schiele
ArtTrack: Articulated Multi-person Tracking in the Wild.
In _Conference on Computer Vision and Pattern Recognition (CVPR)_, 2017**
<p align="center">
<a href="https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/"><img width="50%" src="images/mpilogo-inf-narrow.png"></a>
</p>
For more information visit http://pose.mpi-inf.mpg.de
## Prerequisites
The implementation is in Python 3 and TensorFlow. We recommended using `conda` to install the dependencies.
First, create a Python 3.6 environment:
```bash
conda create -n py36 python=3.6
conda activate py36
```
Then, install basic dependencies with conda:
```bash
conda install numpy scikit-image pillow scipy pyyaml matplotlib cython
```
Install TensorFlow and remaining packages with pip:
```bash
pip install tensorflow-gpu easydict munkres
```
When running training or prediction scripts, please make sure to set the environment variable
`TF_CUDNN_USE_AUTOTUNE` to 0 (see [this ticket](https://github.com/tensorflow/tensorflow/issues/5048)
for explanation).
If your machine has multiple GPUs, you can select which GPU you want to run on
by setting the environment variable, eg. `CUDA_VISIBLE_DEVICES=0`.
## Demo code
Single-Person (if there is only one person in the image)
```
# Download pre-trained model files
$ cd models/mpii
$ ./download_models.sh
$ cd -
# Run demo of single person pose estimation
$ TF_CUDNN_USE_AUTOTUNE=0 python3 demo/singleperson.py
```
Multiple People
```
# Compile dependencies
$ ./compile.sh
# Download pre-trained model files
$ cd models/coco
$ ./download_models.sh
$ cd -
# Run demo of multi person pose estimation
$ TF_CUDNN_USE_AUTOTUNE=0 python3 demo/demo_multiperson.py
```
## Training models
Please follow these [instructions](models/README.md)
## Citation
Please cite ArtTrack and DeeperCut in your publications if it helps your research:
@inproceedings{insafutdinov2017cvpr,
title = {ArtTrack: Articulated Multi-person Tracking in the Wild},
booktitle = {CVPR'17},
url = {http://arxiv.org/abs/1612.01465},
author = {Eldar Insafutdinov and Mykhaylo Andriluka and Leonid Pishchulin and Siyu Tang and Evgeny Levinkov and Bjoern Andres and Bernt Schiele}
}
@article{insafutdinov2016eccv,
title = {DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model},
booktitle = {ECCV'16},
url = {http://arxiv.org/abs/1605.03170},
author = {Eldar Insafutdinov and Leonid Pishchulin and Bjoern Andres and Mykhaylo Andriluka and Bernt Schiele}
}
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matched fp:6f922489a9f3c505, topic:deep-learning, topic:tensorflow
matched fp:6f922489a9f3c505, topic:pose-estimation, readme:computer vision, desc:pose estimation