{"url":"/dataset/nv-vot211","name":"NT-VOT211","full_name":null,"description_markdown":"NT-VOT211 consists of 211 diverse videos, offering 211,000 well-annotated frames with 8 attributes including camera motion, deformation, fast motion, motion blur, tiny target, distractors, occlusion and out-of-view. To the best of our knowledge, it is the largest night-time tracking benchmark to-date that is specifically designed to address unique challenges such as adverse visibility, image blur, and distractors inherent to night-time tracking scenarios. \r\n\r\n\r\nKindly click the 'Homepage' button below to be directed to our official website. There, you can easily access the independent leaderboard through the 'Evaluation on Server' section on our homepage. We offer comprehensive, step-by-step guidance on utilizing our dataset, and we also provide the annotation tools that were instrumental in the creation of this dataset.","description_withheld":null,"homepage":"https://github.com/LiuYuML/NV-VOT211","introduced_date":"2024-10-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/nt-vot211-a-large-scale-benchmark-for-night","title":"NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking","first_author":"Yu Liu","url":null},"license":{"name":"MIT","url":"https://mit-license.org/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Object Tracking","url":"/task/video-object-tracking","datasets_with_task":"/datasets/task/video-object-tracking"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["NT-VOT211"],"data_loaders":[],"num_papers_in_archive":41,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-object-tracking-on-nv-vot211","task":"Video Object Tracking","dataset_variant":"NT-VOT211","rows":43,"metrics":["AUC","Precision"],"first_row_in_archive_order":{"model":"ProContEXT","paper":"/paper/procontext-exploring-progressive-context","metrics":{"AUC":"40.10","Precision":"54.50"},"code_links":[{"title":"jp-lan/procontext","url":"https://github.com/jp-lan/procontext"},{"title":"yangyucheng000/Paper-4","url":"https://github.com/yangyucheng000/Paper-4/tree/main/ProC-KD-main"},{"title":"zhiqic/procontext","url":"https://github.com/zhiqic/procontext"},{"title":"yangyucheng000/papercode-2","url":"https://github.com/yangyucheng000/papercode-2/tree/main/ProC-KD-main"}]},"note":"rows are the archive's own order at snapshot; 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