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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2504.20052 |
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| _version_ | 1866909596581363712 |
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| author | Magera, Floriane Hoyoux, Thomas Castin, Martin Barnich, Olivier Cioppa, Anthony Van Droogenbroeck, Marc |
| author_facet | Magera, Floriane Hoyoux, Thomas Castin, Martin Barnich, Olivier Cioppa, Anthony Van Droogenbroeck, Marc |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can Geometry Save Central Views for Sports Field Registration? Magera, Floriane Hoyoux, Thomas Castin, Martin Barnich, Olivier Cioppa, Anthony Van Droogenbroeck, Marc Computer Vision and Pattern Recognition 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. |
| title | Can Geometry Save Central Views for Sports Field Registration? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.20052 |