# A Two-point Method for PTZ Camera Calibration in Sports

arXiv: https://arxiv.org/abs/1801.09005 · HTML: https://mlsanity.com/paper/1801.09005/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [A Large Scale Homography Benchmark](https://mlsanity.com/paper/2302.09997/) (2023)
- [BroadTrack: Broadcast Camera Tracking for Soccer](https://mlsanity.com/paper/2412.01721/) (2024)
- [Soccer line mark segmentation and classification with stochastic watershed transform](https://mlsanity.com/paper/2108.06432/) (2021)
- [Homography Estimation in Complex Topological Scenes](https://mlsanity.com/paper/2308.01086/) (2023)
- [PTZ-Calib: Robust Pan-Tilt-Zoom Camera Calibration](https://mlsanity.com/paper/2502.09075/) (2025)

## Abstract (arXiv)

Calibrating narrow field of view soccer cameras is challenging because there are very few field markings in the image. Unlike previous solutions, we propose a two-point method, which requires only two point correspondences given the prior knowledge of base location and orientation of a pan-tilt-zoom (PTZ) camera. We deploy this new calibration method to annotate pan-tilt-zoom data from soccer videos. The collected data are used as references for new images. We also propose a fast random forest method to predict pan-tilt angles without image-to-image feature matching, leading to an efficient calibration method for new images. We demonstrate our system on synthetic data and two real soccer datasets. Our two-point approach achieves superior performance over the state-of-the-art method.