{"url":"/dataset/otb-2015","name":"OTB-2015","full_name":null,"description_markdown":"**OTB-2015**, also referred as Visual Tracker Benchmark, is a visual tracking dataset. It contains 100 commonly used video sequences for evaluating visual tracking.\r\nImage Source: [http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html](http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html)","description_withheld":null,"homepage":"http://cvlab.hanyang.ac.kr/tracker_benchmark/","introduced_date":"2015-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Object Tracking Benchmark","first_author":null,"url":"https://doi.org/10.1109/TPAMI.2014.2388226"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Visual Object Tracking","url":"/task/visual-object-tracking","datasets_with_task":"/datasets/task/visual-object-tracking"},{"name":"Visual Tracking","url":"/task/visual-tracking","datasets_with_task":"/datasets/task/visual-tracking"}],"languages":[],"variants":["OTB-2015"],"data_loaders":[{"repo":"https://github.com/lsyhahaha/DataSet","url":"https://github.com/lsyhahaha/DataSet","frameworks":["pytorch"]}],"num_papers_in_archive":182,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-object-tracking-on-otb-2015","task":"Visual Object Tracking","dataset_variant":"OTB-2015","rows":18,"metrics":["AUC","Precision"],"first_row_in_archive_order":{"model":"SPMTrack-B","paper":"/paper/spmtrack-spatio-temporal-parameter-efficient","metrics":{"AUC":"0.727"},"code_links":[{"title":"wenruicai/spmtrack","url":"https://github.com/wenruicai/spmtrack"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/spmtrack-spatio-temporal-parameter-efficient","title":"SPMTrack: Spatio-Temporal Parameter-Efficient Fine-Tuning with Mixture of Experts for Scalable Visual Tracking","date":"2025-03-24","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/samurai-adapting-segment-anything-model-for-1","title":"SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory","date":"2024-11-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/improving-visual-object-tracking-through","title":"Improving Visual Object Tracking through Visual Prompting","date":"2024-09-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/odtrack-online-dense-temporal-token-learning","title":"ODTrack: Online Dense Temporal Token Learning for Visual Tracking","date":"2024-01-03","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-historical-status-prompt-for","title":"HIPTrack: Visual Tracking with Historical Prompts","date":"2023-11-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/seqtrack-sequence-to-sequence-learning-for","title":"Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking","date":"2023-04-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/do-different-tracking-tasks-require-different","title":"Do Different Tracking Tasks Require Different Appearance Models?","date":"2021-07-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":6,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/stmtrack-template-free-visual-tracking-with","title":"STMTrack: Template-free Visual Tracking with Space-time Memory Networks","date":"2021-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":8,"samples_unverified":3,"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/aaa-adaptive-aggregation-of-arbitrary-online","title":"AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee","date":"2020-09-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/scale-equivariance-improves-siamese-tracking","title":"Scale Equivariance Improves Siamese Tracking","date":"2020-07-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":1,"samples_unverified":11,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-distilled-model-for-tracking-and-tracker","title":"Tracking-by-Trackers with a Distilled and Reinforced Model","date":"2020-07-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/gradnet-gradient-guided-network-for-visual","title":"GradNet: Gradient-Guided Network for Visual Object Tracking","date":"2019-09-15","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/visual-tracking-via-adaptive-spatially","title":"Visual Tracking via Adaptive Spatially-Regularized Correlation Filters","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/siamvgg-visual-tracking-using-deeper-siamese","title":"SiamVGG: Visual Tracking using Deeper Siamese Networks","date":"2019-02-07","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/a-twofold-siamese-network-for-real-time","title":"A Twofold Siamese Network for Real-Time Object Tracking","date":"2018-02-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/end-to-end-representation-learning-for","title":"End-to-end representation learning for Correlation Filter based tracking","date":"2017-04-20","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":8,"samples_harvested":47,"samples_ran":18,"samples_unverified":29,"pointer_only_for_licence":13,"papers_with_no_sample_that_ran":2,"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."}