Iterative Kernel Reconstruction For Deep Learning-Based Blind Image Super-Resolution

Automatic sports field registration aims at projecting a given image taken with unknown camera parameters to a known 3D coordinate system in order to obtain higher-level information like the position and speed of players. Existing methods generally detect specific visual landmarks on the field and then use an iterative refinement to get closer to the desired calibration. They are usually only compared in terms of precision on a standard benchmark without considering other metrics. However, execution speed is also important, mainly in the context of live broadcast TV and sports analysis. This work introduces a new automatic field registration method achieving excellent performance on the WorldCup Soccer benchmark, while neither depending on specific visible landmarks nor any refinement, resulting in a very high execution speed. Finally, to complement the usual Soccer benchmark, we introduce a new Swimming Pool registration benchmark which is more challenging for the task at hand. Code and dataset available at https://github.com/njacquelin/sports_field_registration.