# Optimizing Through Learned Errors for Accurate Sports Field Registration

arXiv: https://arxiv.org/abs/1909.08034 · HTML: https://mlsanity.com/paper/1909.08034/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 19 of 19)

- [Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting](https://mlsanity.com/paper/2104.09333/) (2021)
- [Player Tracking and Identification in Ice Hockey](https://mlsanity.com/paper/2110.03090/) (2021)
- [TVCalib: Camera Calibration for Sports Field Registration in Soccer](https://mlsanity.com/paper/2207.11709/) (2022)
- [PnLCalib: Sports Field Registration via Points and Lines Optimization](https://mlsanity.com/paper/2404.08401/) (2024)
- [A Universal Protocol to Benchmark Camera Calibration for Sports](https://mlsanity.com/paper/2404.09807/) (2024)
- [Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration](https://mlsanity.com/paper/2311.10361/) (2023)
- [Puck localization and multi-task event recognition in broadcast hockey videos](https://mlsanity.com/paper/2105.10563/) (2021)
- [Extraction of Positional Player Data from Broadcast Soccer Videos](https://mlsanity.com/paper/2110.11107/) (2021)
- [BroadTrack: Broadcast Camera Tracking for Soccer](https://mlsanity.com/paper/2412.01721/) (2024)
- [Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning](https://mlsanity.com/paper/2101.05388/) (2021)
- [Localization of Ice-Rink for Broadcast Hockey Videos](https://mlsanity.com/paper/2104.10847/) (2021)
- [KaliCalib: A Framework for Basketball Court Registration](https://mlsanity.com/paper/2209.07795/) (2022)
- [Rink-Agnostic Hockey Rink Registration](https://mlsanity.com/paper/2401.01003/) (2023)
- [Can Geometry Save Central Views for Sports Field Registration?](https://mlsanity.com/paper/2504.20052/) (2025)
- [Soccer line mark segmentation and classification with stochastic watershed transform](https://mlsanity.com/paper/2108.06432/) (2021)
- [Accurate Tennis Court Line Detection on Amateur Recorded Matches](https://mlsanity.com/paper/2404.06977/) (2024)
- [ESC: Evolutionary Stitched Camera Calibration in the Wild](https://mlsanity.com/paper/2404.12694/) (2024)
- [PTZ-Calib: Robust Pan-Tilt-Zoom Camera Calibration](https://mlsanity.com/paper/2502.09075/) (2025)
- [ClearLines - Camera Calibration from Straight Lines](https://mlsanity.com/paper/2505.00452/) (2025)

## Abstract (arXiv)

We propose an optimization-based framework to register sports field templates onto broadcast videos. For accurate registration we go beyond the prevalent feed-forward paradigm. Instead, we propose to train a deep network that regresses the registration error, and then register images by finding the registration parameters that minimize the regressed error. We demonstrate the effectiveness of our method by applying it to real-world sports broadcast videos, outperforming the state of the art. We further apply our method on a synthetic toy example and demonstrate that our method brings significant gains even when the problem is simplified and unlimited training data is available.