{"url":"/dataset/ucf-crime","name":"UCF-Crime","full_name":null,"description_markdown":"The UCF-Crime dataset is a large-scale dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety. \r\n\r\nThis dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities.\r\n\r\nSource: [Video Anomaly Dection Dataset](https://webpages.uncc.edu/cchen62/dataset.html)","description_withheld":null,"homepage":"https://www.crcv.ucf.edu/research/real-world-anomaly-detection-in-surveillance-videos/","introduced_date":"2018-01-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/real-world-anomaly-detection-in-surveillance","title":"Real-world Anomaly Detection in Surveillance Videos","first_author":"Waqas Sultani","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Video Anomaly Detection","url":"/task/video-anomaly-detection","datasets_with_task":"/datasets/task/video-anomaly-detection"},{"name":"Small Data Image Classification","url":"/task/small-data","datasets_with_task":"/datasets/task/small-data"},{"name":"Anomaly Detection In Surveillance Videos","url":"/task/anomaly-detection-in-surveillance-videos","datasets_with_task":"/datasets/task/anomaly-detection-in-surveillance-videos"},{"name":"Multiple Instance Learning","url":"/task/multiple-instance-learning","datasets_with_task":"/datasets/task/multiple-instance-learning"}],"languages":[],"variants":["UCF-Crime"],"data_loaders":[],"num_papers_in_archive":142,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on","task":"Anomaly Detection In Surveillance Videos","dataset_variant":"UCF-Crime","rows":21,"metrics":["ROC AUC","Decidability","EER","AUC"],"first_row_in_archive_order":{"model":"STEAD-Base","paper":"/paper/stead-spatio-temporal-efficient-anomaly-1","metrics":{"ROC AUC":"91.34"},"code_links":[{"title":"agao8/STEAD","url":"https://github.com/agao8/STEAD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-detection-on-ucf-crime-1","task":"Anomaly Detection","dataset_variant":"UCF-Crime","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"MULDE-frame-centric-micro-one-class-classification","paper":"/paper/mulde-multiscale-log-density-estimation-via","metrics":{"AUC":"78.5%"},"code_links":[{"title":"jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection","url":"https://github.com/jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/video-anomaly-detection-on-ucf-crime-2","task":"Video Anomaly Detection","dataset_variant":"UCF-Crime","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"MULDE-frame-centric-micro-one-class-classification","paper":"/paper/mulde-multiscale-log-density-estimation-via","metrics":{"AUC":"78.5%"},"code_links":[{"title":"jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection","url":"https://github.com/jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/prodisc-vad-an-efficient-system-for-weakly","title":"ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications","date":"2025-05-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/stead-spatio-temporal-efficient-anomaly-1","title":"STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications","date":"2025-03-11","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/mtfl-multi-timescale-feature-learning-for","title":"MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos","date":"2024-10-08","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/distilling-aggregated-knowledge-for-weakly","title":"Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection","date":"2024-06-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mulde-multiscale-log-density-estimation-via","title":"MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection","date":"2024-03-21","rows_on_this_dataset":3,"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":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/batchnorm-based-weakly-supervised-video","title":"BatchNorm-based Weakly Supervised Video Anomaly Detection","date":"2023-11-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-prompt-enhanced-context-features-for","title":"Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection","date":"2023-06-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/contrastive-regularized-u-net-for-video","title":"Contrastive-Regularized U-Net for Video Anomaly Detection","date":"2023-04-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mgfn-magnitude-contrastive-glance-and-focus","title":"MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection","date":"2022-11-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/self-supervised-sparse-representation-for","title":"Self-supervised Sparse Representation for Video Anomaly Detection","date":"2022-10-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-multi-stream-deep-neural-network-with-late","title":"A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection","date":"2022-03-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mist-multiple-instance-self-training","title":"MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection","date":"2021-04-04","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":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/anomalous-event-recognition-in-videos-based","title":"Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures","date":"2021-02-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/weakly-supervised-video-anomaly-detection","title":"Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning","date":"2021-01-25","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/localizing-anomalies-from-weakly-labeled","title":"Localizing Anomalies from Weakly-Labeled Videos","date":"2020-08-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/weakly-and-partially-supervised-learning","title":"Weakly and Partially Supervised Learning Frameworks for Anomaly Detection","date":"2020-07-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multiple-instance-based-video-anomaly","title":"Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding","date":"2020-07-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/3d-resnet-with-ranking-loss-function-for","title":"3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos","date":"2020-02-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/real-world-anomaly-detection-in-surveillance","title":"Real-world Anomaly Detection in Surveillance Videos","date":"2018-01-12","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":4,"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":6,"samples_harvested":27,"samples_ran":13,"samples_unverified":14,"pointer_only_for_licence":16,"papers_with_no_sample_that_ran":1,"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."}