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mkocabas / pose-residual-network

Code for the Pose Residual Network introduced in 'MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network (ECCV 2018)' paper
https://arxiv.org/abs/1807.04067
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deep-learning deep-neural-networks eccv eccv-2018 keras pose-estimation python tensorflow

readme

Pose Residual Network

This repository contains a Keras implementation of the Pose Residual Network (PRN) presented in our ECCV 2018 paper:

Muhammed Kocabas, Salih Karagoz, Emre Akbas. MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network. In ECCV, 2018. Arxiv

PRN is described in Section 3.2 of the paper.

Getting Started

We have tested our method on COCO Dataset

Prerequisites

python
tensorflow
keras
numpy
tqdm
pycocotools
progress
scikit-image

Installing

  1. Clone this repository: git clone https://github.com/mkocabas/pose-residual-network.git

  2. Install Tensorflow.

  3. pip install -r src/requirements.txt

  4. To download COCO dataset train2017 and val2017 annotations run: bash data/coco.sh. (data size: ~240Mb)

Training

python main.py

For more options take a look at opt.py

Results

Results on COCO val2017 Ground Truth data.

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.894
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.971
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.912
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.875
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.918
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.909
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.972
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.928
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.896
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.947

License

Other Implementations

Pytorch Version

Citation

If you find this code useful for your research, please consider citing our paper:

@Inproceedings{kocabas18prn,
  Title          = {Multi{P}ose{N}et: Fast Multi-Person Pose Estimation using Pose Residual Network},
  Author         = {Kocabas, Muhammed and Karagoz, Salih and Akbas, Emre},
  Booktitle      = {European Conference on Computer Vision (ECCV)},
  Year           = {2018}
}