{"url":"/dataset/fe108","name":"FE108","full_name":null,"description_markdown":"Large-scale single-object tracking dataset, containing 108 sequences with a total length of 1.5 hours. \r\nFE108 provides ground truth annotations on both the frame and event domain. \r\nThe annotation frequency is up to 40Hz and 240Hz for the frame and event domains, respectively. \r\nFE108 is the largest event-frame-based dataset for single object tracking, and also offers the highest annotation frequency in the event domain.","description_withheld":null,"homepage":"","introduced_date":"2021-09-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/object-tracking-by-jointly-exploiting-frame","title":"Object Tracking by Jointly Exploiting Frame and Event Domain","first_author":"Jiqing Zhang","url":null},"license":{"name":"MIT License","url":"https://github.com/Jee-King/ICCV2021_Event_Frame_Tracking/blob/main/LICENSE"},"modalities":[],"tasks":[{"name":"Object Tracking","url":"/task/object-tracking","datasets_with_task":"/datasets/task/object-tracking"},{"name":"Event-based vision","url":"/task/event-based-vision","datasets_with_task":"/datasets/task/event-based-vision"},{"name":"Event-based Motion Estimation","url":"/task/event-based-motion-estimation","datasets_with_task":"/datasets/task/event-based-motion-estimation"}],"languages":[],"variants":["FE108"],"data_loaders":[],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-tracking-on-fe108","task":"Object Tracking","dataset_variant":"FE108","rows":8,"metrics":["Success Rate","Averaged Precision"],"first_row_in_archive_order":{"model":"HR-MonTrack-Base","paper":"/paper/cross-modal-orthogonal-high-rank-augmentation","metrics":{"Averaged Precision":"96.2","Success Rate":"68.5"},"code_links":[{"title":"zhu-zhiyu/nvs_solver","url":"https://github.com/zhu-zhiyu/nvs_solver"},{"title":"ZHU-Zhiyu/High-Rank_RGB-Event_Tracker","url":"https://github.com/ZHU-Zhiyu/High-Rank_RGB-Event_Tracker"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cross-modal-orthogonal-high-rank-augmentation","title":"Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers","date":"2023-07-09","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/object-tracking-by-jointly-exploiting-frame","title":"Object Tracking by Jointly Exploiting Frame and Event Domain","date":"2021-09-19","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/probabilistic-regression-for-visual-tracking","title":"Probabilistic Regression for Visual Tracking","date":"2020-03-27","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/know-your-surroundings-exploiting-scene","title":"Know Your Surroundings: Exploiting Scene Information for Object Tracking","date":"2020-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/190407220","title":"Learning Discriminative Model Prediction for Tracking","date":"2019-04-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/atom-accurate-tracking-by-overlap","title":"ATOM: Accurate Tracking by Overlap Maximization","date":"2018-11-19","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":13,"samples_ran":6,"samples_unverified":7,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}