Introduction

Sports image analysis is one of the domains that has significantly benefited from advances in computer vision. With the growing adoption of machine learning methods new products and tools have emerged that make sports image analysis both accurate and accessible for professional athletes, amateur and junior athletes, and fans all around the world. Sports images serve as crucial visual records, aiding in post-game or live analysis and decision-making. However, very often they are affected by radial distortion that hinders their accurate interpretation and utilization.

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.

Method	SSIM	PSNR	MDLD
Janos and Benesova (2023)	0.852	23.075 dB	0.0172
Li et al. (2019)	0.417	10.919 dB	1.431
Santana-Cendres et al. (2016)	0.714	21.647 dB	0.1132
Our method	0.933	30.098 dB	0.0088

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.