PnLCalib: Sports Field Registration via Points and Lines Optimization
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917326085947392 |
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| author | Gutiérrez-Pérez, Marc Agudo, Antonio |
| author_facet | Gutiérrez-Pérez, Marc Agudo, Antonio |
| contents | Camera calibration in broadcast sports videos presents numerous challenges for accurate sports field registration due to multiple camera angles, varying camera parameters, and frequent occlusions of the field. Traditional search-based methods depend on initial camera pose estimates, which can struggle in non-standard positions and dynamic environments. In response, we propose an optimization-based calibration pipeline that leverages a 3D soccer field model and a predefined set of keypoints to overcome these limitations. Our method also introduces a novel refinement module that improves initial calibration by using detected field lines in a non-linear optimization process. This approach outperforms existing techniques in both multi-view and single-view 3D camera calibration tasks, while maintaining competitive performance in homography estimation. Extensive experimentation on real-world soccer datasets, including SoccerNet-Calibration, WorldCup 2014, and TS-WorldCup, highlights the robustness and accuracy of our method across diverse broadcast scenarios. Our approach offers significant improvements in camera calibration precision and reliability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_08401 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PnLCalib: Sports Field Registration via Points and Lines Optimization Gutiérrez-Pérez, Marc Agudo, Antonio Computer Vision and Pattern Recognition Artificial Intelligence I.2; I.4; I.5 Camera calibration in broadcast sports videos presents numerous challenges for accurate sports field registration due to multiple camera angles, varying camera parameters, and frequent occlusions of the field. Traditional search-based methods depend on initial camera pose estimates, which can struggle in non-standard positions and dynamic environments. In response, we propose an optimization-based calibration pipeline that leverages a 3D soccer field model and a predefined set of keypoints to overcome these limitations. Our method also introduces a novel refinement module that improves initial calibration by using detected field lines in a non-linear optimization process. This approach outperforms existing techniques in both multi-view and single-view 3D camera calibration tasks, while maintaining competitive performance in homography estimation. Extensive experimentation on real-world soccer datasets, including SoccerNet-Calibration, WorldCup 2014, and TS-WorldCup, highlights the robustness and accuracy of our method across diverse broadcast scenarios. Our approach offers significant improvements in camera calibration precision and reliability. |
| title | PnLCalib: Sports Field Registration via Points and Lines Optimization |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2; I.4; I.5 |
| url | https://arxiv.org/abs/2404.08401 |