HyperStackNet: A Hyper Stacked Hourglass Deep Convolutional Neural Network Architecture for Joint Player and Stick Pose Estimation in Hockey

This is the training pipeline used for:

H. Neher, K. Vats, A. Wong, D. A. Clausi, HyperStackNet: A Hyper Stacked Hourglass Deep Convolutional Neural Network Architecture for Joint Player and Stick Pose Estimation in Hockey, CRV, 2018.

To run this code, make sure the following are installed:

• Torch7
• hdf5
• cudnn

Getting Started

Download the full MPII Human Pose dataset, and place the images directory in data/mpii. From there, it is as simple as running th main.lua -expID test-run (the experiment ID is arbitrary). To run on FLIC, again place the images in a directory data/flic/images then call th main.lua -dataset flic -expID test-run.

Most of the command line options are pretty self-explanatory, and can be found in src/opts.lua. The -expID option will be used to save important information in a directory like pose-hg-train/exp/mpii/test-run. This directory will include snapshots of the trained model, training/validations logs with loss and accuracy information, and details of the options set for that particular experiment.

Acknowledgements

Code was modified from:

Alejandro Newell, Kaiyu Yang, and Jia Deng, Stacked Hourglass Networks for Human Pose Estimation, arXiv:1603.06937, 2016.