Introduction

The source code of our method can be found at – https://github.com/IgorJanos/stuFieldReg-official

The official repository of the Calib360 dataset can be found at – https://github.com/IgorJanos/stuCalib360

The best pre-trained models can be downloaded at – https://vggnas.fiit.stuba.sk/download/janos/fieldreg/fieldreg-experiments.tar.gz

You can find our original paper at – … URL …

Dataset

We propose the Calib360 dataset, which contains 300,000 training images with accurate homography annotations, which we have constructed from equirectangular panoramas. In panoramas, we annotate the positions of known keypoints of the football playing field model.

Next, from the annotated keypoints we extract the panoramic camera position and orientation with respect to the playing field. Once the camera parameters are extracted, we can generate unlimited number of different views of the playing field by adjusting the viewing angles and field of view. These generated images are perfect for training of neural networks because they all conform to the pinhole camera model and contain accurate annotations.

Results

Estimated playing field registration by our method (first column), by the method of Nie et al. (2021, second column), by the method of Chu et al. (2022, third column), and by the method of Theiner et al. (2023, fourth column).

Introduction

We propose a deep-learning regression-based method to rectify images containing radial lens distortion, that operates on single independent images. Our method hase been trained on the football domain dataset, and has been fine-tuned specifically for images containing small radial distortion, which is more difficult to rectify accurately. Compared to other deep-learning radial distortion correction methods, we introduce the following contributions:

• Secondary learning task that learns useful distortion features on random combinations of sample pairs, prevents overfitting, and encourages the feature extractor to learn more general distortion features.
• Penalty term that encourages better accuracy on low-distortion images

The official GitHub repository for our method can be found at – https://github.com/IgorJanos/stuImprovingRadial-official

The best trained model and training configuration can be downloaded here – improving-radial-best.tar.gz

The original paper can be found here – https://doi.org/10.1016/j.patrec.2024.05.008

Results

We have performed a quantitative evaluation of our proposed method on the validation set of the Football360 validation subset, and compared our method with 3 other methods. Our method outperformed all other evaluated methods on all metrics by a significant margin.

We have performed a qualitative evaluation of our method on the World Cup 2014 dataset. The WorldCup 2014 images have not been used during the training of neither of the evaluated methods.

Input images	Corrected by our method	Janos and Benesova (2023)	Li (2019)	Santana-Cedres (2016)

Introduction

The aim of this dataset is to aid the development and evaluation of computer vision algorithms, primarily the radial distortion correction algorithms, in the sports domain.

This dataset contains 268 panorama images, and was created using the PANONO panoramic camera in 3 football arenas in Slovakia. Each arena was covered from numerous locations on all levels of the tribune, and broadcast camera platforms. The images capture regular football game, pitch maintenance, low/challenging lighting conditions, day and night situations.

You can download the raw images from the following link – football360-raw.tar.gz (7.7 GB)

There is also an official GitHub repository available at – https://github.com/IgorJanos/stuFootball360

The original paper can be found here – https://www.scitepress.org/PublishedPapers/2023/116812/116812.pdf

Raw Images

Raw images are stored as 16384×8192 JPG, they are the direct result of the PANONO stitching service.

Exported Sets

There are four exported datasets available for direct download and use. Each dataset contains a collection of images (data) distorted with known distortion coefficients (labels).

You can see an example of how to load and use these files in a linked notebook – https://github.com/IgorJanos/stuFootball360/blob/main/notebooks/explore.ipynb

Distorted Image	Corrected Image

Igor Janos