{"url":"/dataset/itb","name":"ITB","full_name":"Informative Tracking Benchmark","description_markdown":"**Informative Tracking Benchmark** (**ITB**) is a small and informative tracking benchmark with 7% out of 1.2 M frames of existing and newly collected datasets, which enables efficient evaluation while ensuring effectiveness. Specifically, the authors designed a quality assessment mechanism to select the most informative sequences from existing benchmarks taking into account 1) challenging level, 2) discriminative strength, 3) and density of appearance variations. Furthermore, they collect additional sequences to ensure the diversity and balance of tracking scenarios, leading to a total of 20 sequences for each scenario.","description_withheld":null,"homepage":"https://github.com/XinLi-zn/Informative-tracking-benchmark","introduced_date":"2021-12-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/an-informative-tracking-benchmark","title":"An Informative Tracking Benchmark","first_author":"Xin Li","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Object Tracking","url":"/task/object-tracking","datasets_with_task":"/datasets/task/object-tracking"},{"name":"Visual Object Tracking","url":"/task/visual-object-tracking","datasets_with_task":"/datasets/task/visual-object-tracking"}],"languages":[],"variants":["ITB"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-object-tracking-on-itb","task":"Visual Object Tracking","dataset_variant":"ITB","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"DropTrack","paper":"/paper/dropmae-masked-autoencoders-with-spatial","metrics":{"AUC":"0.65"},"code_links":[{"title":"jimmy-dq/dropmae","url":"https://github.com/jimmy-dq/dropmae"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dropmae-masked-autoencoders-with-spatial","title":"DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks","date":"2023-04-02","rows_on_this_dataset":1,"code_links":1,"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."}