{"url":"/dataset/sbdt-soccer","name":"Soccer","full_name":"ISSIA-CNR Soccer","description_markdown":"- This dataset was originally introduced by [1] for soccer ball and player tracking from six synchronized videos. \r\n- Since ball annotations provided by [1] are collapsed, new annotations of ball 2D coordinates are provided by [2]\r\n- For sports ball detection and tracking evaluation, the first four video clips are used for training and the remaining two clips are for testing.\r\n\r\n[1] T. D’Orazio et al., A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences, in AVSS, 2009.\r\n[2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, in BMVC, 2023.","description_withheld":null,"homepage":"https://github.com/nttcom/WASB-SBDT/blob/main/GET_STARTED.md#data-preparation","introduced_date":"2009-10-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-semi-automatic-system-for-ground-truth","title":"A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences","first_author":"T. D'Orazio","url":null},"license":null,"modalities":[],"tasks":[{"name":"Sports Ball Detection and Tracking","url":"/task/sports-ball-detection-and-tracking","datasets_with_task":"/datasets/task/sports-ball-detection-and-tracking"},{"name":"Reinforcement Learning","url":"/task/reinforcement-learning","datasets_with_task":"/datasets/task/reinforcement-learning"}],"languages":[],"variants":["Soccer"],"data_loaders":[],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sports-ball-detection-and-tracking-on-sbdt","task":"Sports Ball Detection and Tracking","dataset_variant":"Soccer","rows":8,"metrics":["F1 (%)","Accuracy (% )","Average Precision (%)"],"first_row_in_archive_order":{"model":"WASB (Step=3)","paper":"/paper/widely-applicable-strong-baseline-for-sports","metrics":{"Accuracy (% )":"97.9","Average Precision (%)":"83.6","F1 (%)":"88.3"},"code_links":[{"title":"nttcom/wasb-sbdt","url":"https://github.com/nttcom/wasb-sbdt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/widely-applicable-strong-baseline-for-sports","title":"Widely Applicable Strong Baseline for Sports Ball Detection and Tracking","date":"2023-11-09","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/monotrack-shuttle-trajectory-reconstruction","title":"MonoTrack: Shuttle trajectory reconstruction from monocular badminton video","date":"2022-04-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/tracknetv2-efficient-shuttlecock-tracking","title":"TrackNetV2: Efficient Shuttlecock Tracking Network","date":"2020-12-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/real-time-cnn-based-segmentation-architecture","title":"Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup","date":"2020-07-23","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deepball-deep-neural-network-ball-detector","title":"DeepBall: Deep Neural-Network Ball Detector","date":"2019-02-19","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}