{"url":"/dataset/coesot","name":"COESOT","full_name":null,"description_markdown":"In this work, we propose a general dataset for Color-Event camera based Single Object Tracking, termed COESOT. It contains 1354 color-event videos with 478,721 RGB frames. We split them into a training and testing subset, which contains 827 and 527 videos, respectively. The videos are collected from both outdoor and indoor scenarios (such as the street, zoo, and home) using the DAVIS346 event camera with a zoom lens. Therefore, our videos can reflect the variation in the distance at depth, but other datasets are failed to. Different from existing benchmarks which contain limited categories, our proposed COESOT covers a wider range of object categories (90 classes), as shown in Fig. 3 (a). It mainly reflects four groups, including persons, animals, electronics, and other goods.\r\n\r\nThe ground truth of the proposed COESOT dataset is densely annotated, i.e., in a frame-by frame way. The absent label is also provided to help researchers design their trackers. Inspired by VisEvent [44], we annotate each testing video sequence with 17 attributes to help researchers evaluate their trackers in specific challenging environments, e.g., full occlusion (FOC), deformation (DEF), rotation (ROT), fast motion (FM), partially occlusion (POC), low illumination (LI), scale variation (SV), background object motion (BOM), motion blur (MB), overexposure (OE), etc. The distribution of videos in each attribute is shown in Fig. 3 (b). The statistical distribution of the ground truth center position is shown in Fig 3 (c). More details can be found in our supplementary materials.","description_withheld":null,"homepage":"https://github.com/Event-AHU/COESOT","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"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":["COESOT"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-tracking-on-coesot","task":"Object Tracking","dataset_variant":"COESOT","rows":12,"metrics":["Success Rate","Precision Rate"],"first_row_in_archive_order":{"model":"HR-CEUTrack-Large","paper":"/paper/cross-modal-orthogonal-high-rank-augmentation","metrics":{"Precision Rate":"73.8","Success Rate":"65.0"},"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/revisiting-color-event-based-tracking-a","title":"Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric","date":"2022-11-20","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/aiatrack-attention-in-attention-for","title":"AiATrack: Attention in Attention for Transformer Visual Tracking","date":"2022-07-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/joint-feature-learning-and-relation-modeling","title":"Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework","date":"2022-03-22","rows_on_this_dataset":1,"code_links":1,"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/learning-target-candidate-association-to-keep","title":"Learning Target Candidate Association to Keep Track of What Not to Track","date":"2021-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/2103-15436","title":"Transformer Tracking","date":"2021-03-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/transformer-meets-tracker-exploiting-temporal","title":"Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking","date":"2021-03-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/siam-r-cnn-visual-tracking-by-re-detection","title":"Siam R-CNN: Visual Tracking by Re-Detection","date":"2019-11-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"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":6,"samples_harvested":21,"samples_ran":8,"samples_unverified":13,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":3,"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."}