snie2012 / mxnet_Realtime_Multi-Person_Pose_Estimation

This is a mxnet version of Realtime_Multi-Person_Pose_Estimation, origin code is here https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation

Reimplementation of human keypoint detection in mxnet

1. You can download mxnet model and parameters(coco and MPII) from google drive:

   https://drive.google.com/drive/folders/0BzffphMuhDDMV0RZVGhtQWlmS1U

   or check caffe_to_mxnet folder to download original caffe model and transfer it to mxnet model.

2. Test demo based on model of coco dataset: testModel.ipynb

3. Test demo based on model of MPII dataset: testModel_mpi.ipynb

4. Train demo: TrainWeight.py

5. Evaluation on coco validation dataset : evaluation_coco.py

Cite paper Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

@article{cao2016realtime,
  title={Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
  author={Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  journal={arXiv preprint arXiv:1611.08050},
  year={2016}
  }

original caffe training https://github.com/CMU-Perceptual-Computing-Lab/caffe_rtpose

TODO:

• Test demo
• Train demo
• Add image augmentation: rotation, flip
• Add weight vector
• Train all images
• Train from vgg model
• evaluation code
• image read and augmentation in C++

Other implementations

Original caffe training model https://github.com/CMU-Perceptual-Computing-Lab/caffe_rtpose

Original data preparation https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation

pytorch https://github.com/tensorboy/pytorch_Realtime_Multi-Person_Pose_Estimation

keras https://github.com/raymon-tian/keras_Realtime_Multi-Person_Pose_Estimation