Can Geometry Save Central Views for Sports Field Registration?
Not on the map. Why?
Abstract (arXiv)
Single-frame sports field registration often serves as the foundation for extracting 3D information from broadcast videos, enabling applications related to sports analytics, refereeing, or fan engagement. As sports fields have rigorous specifications in terms of shape and dimensions of their line, circle and point components, sports field markings are commonly used as calibration targets for this task. However, because of the sparse and uneven distribution of field markings, close-up camera views around central areas of the field often depict only line and circle markings. On these views, sports field registration is challenging for the vast majority of existing methods, as they focus on leveraging line field markings and their intersections. It is indeed a challenge to include circle correspondences in a set of linear equations. In this work, we propose a novel method to derive a set of points and lines from circle correspondences, enabling the exploitation of circle correspondences for both sports field registration and image annotation. In our experiments, we illustrate the benefits of our bottom-up geometric method against top-performing detectors and show that our method successfully complements them, enabling sports field registration in difficult scenarios.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 10 Apr 2025.
Builds on
14 of 40 references.
References · 14
- 2019Understanding the Limitations of CNN-based Absolute Camera Pose Regressioncited by 177
- 2016Deep Image Homography Estimationcited by 136
- 2023VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Viewscited by 38
- 2024SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimapcited by 34
- 2018Sports Camera Calibration via Synthetic Datacited by 26
- 2022TVCalib: Camera Calibration for Sports Field Registration in Soccercited by 22
- 2019Optimizing Through Learned Errors for Accurate Sports Field Registrationcited by 19
- 2017Automated Top View Registration of Broadcast Football Videoscited by 15
- 2024PnLCalib: Sports Field Registration via Points and Lines Optimizationcited by 14
- 2024A Universal Protocol to Benchmark Camera Calibration for Sportscited by 14
- 2023Video-based Sequential Bayesian Homography Estimation for Soccer Field Registrationcited by 8
- 2024Enhancing Soccer Camera Calibration Through Keypoint Exploitationcited by 7
2 more
Built on by
Citing papers per year: 2026: 2.
2 papers.
- 2026Towards Athlete Fatigue Assessment from Association Football Videoscited by 1
- 2026SoccerNet 2026 Challenges Resultscited by 1
- 2026SoccerNet 2026 Challenges Results8 Jul 2026
- 2026Towards Athlete Fatigue Assessment from Association Football Videos7 Apr 2026