{"url":"/dataset/otb","name":"OTB","full_name":null,"description_markdown":"Object Tracking Benchmark (**OTB**) is a visual tracking benchmark that is widely used to evaluate the performance of a visual tracking algorithm. The dataset contains a total of 100 sequences and each is annotated frame-by-frame with bounding boxes and 11 challenge attributes. [OTB-2013](otb-2013) dataset contains 51 sequences and the [OTB-2015](otb-2015) dataset contains all 100 sequences of the OTB dataset.\r\n\r\nSource: [Deep Meta Learning for Real-Time Target-Aware Visual Tracking](https://arxiv.org/abs/1712.09153)","description_withheld":null,"homepage":"http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html","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":"Videos","url":"/datasets/modality/videos"},{"name":"Tracking","url":"/datasets/modality/tracking"}],"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","OTB-2013","OTB-50","OTB"],"data_loaders":[],"num_papers_in_archive":416,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-object-tracking-on-otb-50","task":"Visual Object Tracking","dataset_variant":"OTB-50","rows":4,"metrics":["AUC"],"first_row_in_archive_order":{"model":"SiamVGG","paper":"/paper/siamvgg-visual-tracking-using-deeper-siamese","metrics":{"AUC":"0.61"},"code_links":[{"title":"zllrunning/SiameseX.PyTorch","url":"https://github.com/zllrunning/SiameseX.PyTorch"},{"title":"leeyeehoo/SiamVGG","url":"https://github.com/leeyeehoo/SiamVGG"},{"title":"logiklesuraj/siamfcex","url":"https://github.com/logiklesuraj/siamfcex"},{"title":"logiklesuraj/SiamFC","url":"https://github.com/logiklesuraj/SiamFC"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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},{"paper":"/paper/fully-convolutional-siamese-networks-for-1","title":"Fully-Convolutional Siamese Networks for Object Tracking","date":"2016-06-30","rows_on_this_dataset":1,"code_links":10,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"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."}