{"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/real-time-cnn-based-segmentation-architecture","title":"Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup","arxiv_id":"2007.11876","date":"2020-07-23","proceeding":null,"authors":["Gabriel Van Zandycke","Christophe De Vleeschouwer"],"abstract":"This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players while being poorly contrasted with respect to the background. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. To take advantage of the ball dynamics, the network is fed with a pair of consecutive images. Our inference model can run in real time without the delay induced by a temporal analysis. We also show that test-time data augmentation allows for a significant increase the detection accuracy. As an additional contribution, we publicly release the dataset on which this work is based.","url_abs":"https://arxiv.org/abs/2007.11876v1","url_pdf":"https://arxiv.org/pdf/2007.11876v1.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":"real-time-cnn-based-segmentation-architecture","repo_url":"https://github.com/pacifinapacific/awesome_sportanalysis_paper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"real-time-cnn-based-segmentation-architecture","repo_url":"https://github.com/nttcom/wasb-sbdt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"sports-ball-detection-and-tracking","task_name":"Sports Ball Detection and Tracking"}],"methods":[],"datasets_introduced":[{"slug":"deepsport-dataset","name":"DeepSport Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/sports-ball-detection-and-tracking-on","task":"Sports Ball Detection and Tracking","dataset":"Badminton","model":"BallSeg","rank_in_archive_order":6,"of":8,"metrics":{"Accuracy (%)":"72.2","Average Precision (%)":"68.4","F1 (%)":"79.9"},"uses_additional_data":false},{"leaderboard":"/sota/sports-ball-detection-and-tracking-on-2","task":"Sports Ball Detection and Tracking","dataset":"Basketball","model":"BallSeg","rank_in_archive_order":7,"of":8,"metrics":{"Accuracy (%)":"20.5","Average Precision (%)":"5.3","F1 (%)":"16.8"},"uses_additional_data":false},{"leaderboard":"/sota/sports-ball-detection-and-tracking-on-sbdt","task":"Sports Ball Detection and Tracking","dataset":"Soccer","model":"BallSeg","rank_in_archive_order":8,"of":8,"metrics":{"Accuracy (% )":"92.6","Average Precision (%)":"20.0","F1 (%)":"36.1"},"uses_additional_data":false},{"leaderboard":"/sota/sports-ball-detection-and-tracking-on-tennis","task":"Sports Ball Detection and Tracking","dataset":"Tennis","model":"BallSeg","rank_in_archive_order":6,"of":8,"metrics":{"Accuracy (%)":"57.5","Average Precision (%)":"56.8","F1 (%)":"71.7"},"uses_additional_data":false},{"leaderboard":"/sota/sports-ball-detection-and-tracking-on-1","task":"Sports Ball Detection and Tracking","dataset":"Volleyball","model":"BallSeg","rank_in_archive_order":8,"of":8,"metrics":{"Accuracy (%)":"17.5","Average Precision (%)":"8.5","F1 (%)":"19.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}