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. 2022 Jun 21;9:355. doi: 10.1038/s41597-022-01469-1

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — SoccerNet-v3 annotations. In each replay sequence of an action within 400 of SoccerNet’s full broadcast games, we select the replay frame showing the live action frame (top). Then, on all those replay and live action frames, we annotate lines and goal parts (left), classified into respectively 20 and 6 classes, illustrated in different colors. We further enclose each human on the field within a bounding box, we specify players team, and annotate salient objects (middle). Finally, we establish multi-view player correspondences across replay and live action frames of the same scene, using jersey numbers whenever possible (right). SoccerNet-v3 thus provides a rich environment to explore soccer-related computer vision tasks.