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Optimization Based Image Registration

Optimizing Through Learned Errors for Accurate Sports Field Registration - WACV 2020

This repository is a reference implementation for the inference part of "Optimizing Through Learned Errors for Accurate Sports Field Registration", WACV 2020. For more details, please refer to our WACV 2020 or [arXiv] paper. A video showing the results is available [here]

teaser

The released code is freely available for free non-commercial academic research use, and may be redistributed under these conditions. Any commercial use is prohibited.

Note: We decided to not release the training code. Sorry for any inconvenience.

Patent Pending

The Optimization Based Image Registration is patent protected (pending applications US 62/850,910; US 16/049,546; EP17746676.0; CA 3,012,721)[here] and shall not be used for any commercial application. For information about licensing please contact If you are interested in a commercial license, contact Sportlogiq or SLiQ Labs.

Content of the repository

  1. The trained weights (both initial guess net, and loss surface net) for soccer.
  2. The inference code for soccer.
  3. A Jupiter notebook to for simple user interaction.
  4. The code to generate a soccer field template(Processing language) and a h5 format test dataset used in the paper.

Installation

This implementation is based on Python3 and PyTorch.

You can install the environment by: conda env create -f environment.yml

Activate the env by: conda activate sportsfield

Pretrained Weights

We provide the pretrained weights for soccer on Google drive. Download "out.zip", and extract all the content to ./out, such that the ./out folder contains pretrained_init_guess and pretrained_loss_surface .

Play with jupyter notebook

Users can overlay the template to a soccer image or video using the notebook.

Evaluation

Users can simply run: python test_end2end.py loss_surface init_guess --load_weights_upstream "pretrained_init_guess" --load_weights_error_model "pretrained_loss_surface" --batch_size 32 to start the evaluation.

A reference evaluation result is provided for comparison:

----- Summary -----
original IOU part mean: 0.90211654
original IOU part median: 0.91872334
original IOU whole mean: 0.8406853
original IOU whole median: 0.857767
optimized IOU part mean: 0.9530167
optimized IOU part median: 0.9701195
optimized IOU whole mean: 0.9019278
optimized IOU whole median: 0.9253305
----- -----
spent 290.74491572380066 seconds for 186 images
1.5631447081924768 seconds per single image
----- End -----

Citation

If you use this code in your research, cite the paper:

@inproceedings{jiang2020optimizing,
author={Wei Jiang and Juan Camilo Gamboa Higuera and Baptiste Angles and Weiwei Sun and Mehrsan Javan and Kwang Moo Yi},
booktitle={2020 IEEE Winter Conference on Applications of Computer Vision (WACV)},
title={Optimizing Through Learned Errors for Accurate Sports Field Registration},
year={2020},
organization={IEEE}
}

License

The released code is freely available for free non-commercial academic research use, and may be redistributed under these conditions. Please, see the license for further details. If you are interested in a commercial license, contact Sportlogiq or SLiQ Labs for licensing information.

Note: The Optimization Based Image Registration is patent protected (pending applications US 62/850,910; US 16/049,546; EP17746676.0; CA 3,012,721) and shall not be used for any commercial application.

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Code release for WACV 2020, "Optimizing Through Learned Errors for Accurate Sports Field Registration"

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