{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sports-camera-calibration-via-synthetic-data","title":"Sports Camera Calibration via Synthetic Data","arxiv_id":"1810.10658","date":"2018-10-25","proceeding":null,"authors":["Jianhui Chen","James J. Little"],"abstract":"Calibrating sports cameras is important for autonomous broadcasting and\nsports analysis. Here we propose a highly automatic method for calibrating\nsports cameras from a single image using synthetic data. First, we develop a\nnovel camera pose engine. The camera pose engine has only three significant\nfree parameters so that it can effectively generate a lot of camera poses and\ncorresponding edge (i.e, field marking) images. Then, we learn compact deep\nfeatures via a siamese network from paired edge image and camera pose and build\na feature-pose database. After that, we use a novel two-GAN (generative\nadversarial network) model to detect field markings in real images. Finally, we\nquery an initial camera pose from the feature-pose database and refine camera\nposes using truncated distance images. We evaluate our method on both synthetic\nand real data. Our method not only demonstrates the robustness on the synthetic\ndata but also achieves the state-of-the-art accuracy on a standard soccer\ndataset and very high performance on a volleyball dataset.","url_abs":"http://arxiv.org/abs/1810.10658v1","url_pdf":"http://arxiv.org/pdf/1810.10658v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sports-camera-calibration-via-synthetic-data","repo_url":"https://github.com/ashura1234/soccer-line-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"sports-camera-calibration-via-synthetic-data","repo_url":"https://github.com/lood339/SCCvSD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"sports-camera-calibration-via-synthetic-data","repo_url":"https://github.com/xecarlox94/Computational-Imaging","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"camera-calibration","task_name":"Camera Calibration"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"sports-analytics","task_name":"Sports Analytics"}],"methods":[{"method_slug":"siamese-network","method_name":"Siamese Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}