{"url":"/dataset/labelling-for-explosions-and-road-accidents","name":"Labelling for Explosions and Road accidents from UCF-Crime","full_name":null,"description_markdown":"The whole UCF-Crime dataset consists of real-world 240 × 320 RGB videos with 13 realistic anomaly types such as explosion, road accident, burglary, etc., and normal examples. The CPD specific requires a change in data distribution. We suppose that explosions and road accidents correspond to such a scenario, while most other types correspond to point anomalies. For example, data, obviously, com from a normal regime before the explosion. After it, we can see fire and smoke, which last for some time. Thus, the first moment when an explosion appears is a change point. Along with a volunteer, the authors carefully labelled chosen anomaly types. Their opinions were averaged. We provide the obtained markup, so other researchers can use it to validate their CPD algorithm for video.","description_withheld":null,"homepage":"","introduced_date":"2021-06-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/principled-change-point-detection-via","title":"InDiD: Instant Disorder Detection via Representation Learning","first_author":"Evgenia Romanenkova","url":null},"license":null,"modalities":[],"tasks":[{"name":"Change Point Detection","url":"/task/change-point-detection","datasets_with_task":"/datasets/task/change-point-detection"}],"languages":[],"variants":["Labelling for Explosions and Road accidents from UCF-Crime"],"data_loaders":[{"repo":"https://github.com/romanenkova95/InDiD","url":"https://github.com/romanenkova95/InDiD","frameworks":["pytorch"]}],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}