https://github.com/eldar/deepcut
Multi Person Pose Estimation
https://github.com/eldar/deepcut
Last synced: about 1 year ago
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Multi Person Pose Estimation
- Host: GitHub
- URL: https://github.com/eldar/deepcut
- Owner: eldar
- Created: 2016-06-24T05:39:52.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2016-08-30T15:02:12.000Z (almost 10 years ago)
- Last Synced: 2025-02-16T08:27:54.706Z (over 1 year ago)
- Language: Matlab
- Size: 275 KB
- Stars: 222
- Watchers: 20
- Forks: 83
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
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README
# Deep(er)Cut: Multi Person Pose Estimation
This short documentation describes steps necessary to compile and run the code that implements [DeepCut](http://arxiv.org/abs/1511.06645) and [DeeperCut](http://arxiv.org/abs/1605.03170) papers:
**Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, and Bernt Schiele
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
In _IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, 2016**
**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**
For more information visit http://pose.mpi-inf.mpg.de
## Prerequisites
- This code was developed under Linux (Debian wheezy, 64 bit) and was tested only in this environment.
- HDF5 1.8
- CMake
- C++ 11
- CUDA >=7.5
- [Caffe building instructions](http://caffe.berkeleyvision.org/installation.html)
- [Gurobi optimizer 6.0.x](https://user.gurobi.com/download/gurobi-optimizer)
## Installation Instructions
1. Clone repository
```
$ git clone https://github.com/eldar/deepcut --recursive
```
2. Build Caffe and its MATLAB interface after configuring `Makefile.config`
```
$ cd external/caffe
$ make -j 4 all matcaffe
```
3. Build `liblinear`, specify the path to the MATLAB installation
```
$ cd external/liblinear-1.94/matlab
$ CC=gcc CXX=g++ MATLABDIR=/usr/lib/matlab-8.6/ make
```
4. Build solver
```
$ cd external/solver
$ cmake . -DGUROBI_ROOT_DIR=/path/to/gurobi603/linux64 -DGUROBI_VERSION=60
$ make solver-callback
```
5. Obtain Gurobi license from http://www.gurobi.com/downloads/licenses/license-center
and place the license file license.lic in data/gurobi or modify parameter
p.gurobi_license_file in lib/pose/exp_params.m to point to the license file location
## Download models
```
$ cd data
$ ./download_models.sh
```
## Run Demo
```
$ cd
$ ./start_matlab.sh
% in MATLAB
>> demo_multiperson
```
## CNN-based part detectors
Access [DeeperCut Part Detectors](https://github.com/eldar/deepcut-cnn) to download stand-alone part detectors that produce dense scoremaps.
## Citing
```
@inproceedings{insafutdinov2016deepercut,
author = {Eldar Insafutdinov and Leonid Pishchulin and Bjoern Andres and Mykhaylo Andriluka and Bernt Schieke},
title = {DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2016},
url = {http://arxiv.org/abs/1605.03170}
}
@inproceedings{pishchulin16cvpr,
author = {Leonid Pishchulin and Eldar Insafutdinov and Siyu Tang and Bjoern Andres and Mykhaylo Andriluka and Peter Gehler and Bernt Schiele},
title = {DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016},
url = {http://arxiv.org/abs/1511.06645}
}
```