{"url":"/dataset/ucsd","name":"UCSD Ped2","full_name":"UCSD Anomaly Detection Dataset","description_markdown":"The **UCSD** Anomaly Detection Dataset was acquired with a stationary camera mounted at an elevation, overlooking pedestrian walkways. The crowd density in the walkways was variable, ranging from sparse to very crowded. In the normal setting, the video contains only pedestrians. Abnormal events are due to either:\r\nthe circulation of non pedestrian entities in the walkways\r\nanomalous pedestrian motion patterns\r\nCommonly occurring anomalies include bikers, skaters, small carts, and people walking across a walkway or in the grass that surrounds it. A few instances of people in wheelchair were also recorded. All abnormalities are naturally occurring, i.e. they were not staged for the purposes of assembling the dataset. The data was split into 2 subsets, each corresponding to a different scene. The video footage recorded from each scene was split into various clips of around 200 frames.\r\n\r\nSource: [The UCSD Anomaly Detection Dataset](http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm)\r\nImage Source: [http://www.svcl.ucsd.edu/publications/conference/2010/cvpr2010/cvpr_anomaly_2010.pdf](http://www.svcl.ucsd.edu/publications/conference/2010/cvpr2010/cvpr_anomaly_2010.pdf)","description_withheld":null,"homepage":"http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm","introduced_date":"2010-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Anomaly detection in crowded scenes","first_author":null,"url":"https://doi.org/10.1109/CVPR.2010.5539872"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"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":"Multimodal Activity Recognition","url":"/task/multimodal-activity-recognition","datasets_with_task":"/datasets/task/multimodal-activity-recognition"},{"name":"Abnormal Event Detection In Video","url":"/task/abnormal-event-detection-in-video","datasets_with_task":"/datasets/task/abnormal-event-detection-in-video"}],"languages":[],"variants":["UCSD Ped2","UCSD-MIT Human Motion"],"data_loaders":[],"num_papers_in_archive":92,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-ucsd-ped2","task":"Anomaly Detection","dataset_variant":"UCSD Ped2","rows":14,"metrics":["AUC","FPS"],"first_row_in_archive_order":{"model":"DMAD","paper":"/paper/diversity-measurable-anomaly-detection","metrics":{"AUC":"99.7%"},"code_links":[{"title":"FlappyPeggy/DMAD","url":"https://github.com/FlappyPeggy/DMAD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/abnormal-event-detection-in-video-on-ucsd","task":"Abnormal Event Detection In Video","dataset_variant":"UCSD Ped2","rows":4,"metrics":["AUC"],"first_row_in_archive_order":{"model":"AI-VAD","paper":"/paper/attribute-based-representations-for-accurate","metrics":{"AUC":"99.1"},"code_links":[{"title":"openvinotoolkit/anomalib","url":"https://github.com/openvinotoolkit/anomalib/tree/main/src/anomalib/models/ai_vad"},{"title":"talreiss/Mean-Shifted-Anomaly-Detection","url":"https://github.com/talreiss/Mean-Shifted-Anomaly-Detection"},{"title":"talreiss/accurate-interpretable-vad","url":"https://github.com/talreiss/accurate-interpretable-vad"},{"title":"talreiss/PANDA","url":"https://github.com/talreiss/PANDA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/video-anomaly-detection-on-ucsd-ped2-1","task":"Video Anomaly Detection","dataset_variant":"UCSD Ped2","rows":3,"metrics":["AUC"],"first_row_in_archive_order":{"model":"MULDE-object-centric-micro","paper":"/paper/mulde-multiscale-log-density-estimation-via","metrics":{"AUC":"99.7%"},"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/multimodal-activity-recognition-on-ucsd-mit","task":"Multimodal Activity Recognition","dataset_variant":"UCSD-MIT Human Motion","rows":1,"metrics":["F1-score"],"first_row_in_archive_order":{"model":"HAMLET","paper":"/paper/hamlet-a-hierarchical-multimodal-attention-1","metrics":{"F1-score":"81.52"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/vadmamba-exploring-state-space-models-for","title":"VADMamba: Exploring State Space Models for Fast Video Anomaly Detection","date":"2025-03-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/uninet-a-contrastive-learning-guided-unified","title":"UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection","date":"2025-02-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/follow-the-rules-reasoning-for-video-anomaly","title":"Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models","date":"2024-07-14","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":2,"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/making-anomalies-more-anomalous-video-anomaly","title":"Making Anomalies More Anomalous: Video Anomaly Detection Using a Novel Generator and Destroyer","date":"2024-02-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vald-gan-video-anomaly-detection-using-latent","title":"VALD-GAN: video anomaly detection using latent discriminator augmented GAN","date":"2023-10-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/stemgan-spatio-temporal-generative","title":"STemGAN: spatio-temporal generative adversarial network for video anomaly detection","date":"2023-09-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/self-distilled-masked-auto-encoders-are","title":"Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors","date":"2023-06-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/diversity-measurable-anomaly-detection","title":"Diversity-Measurable Anomaly Detection","date":"2023-03-09","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":6,"samples_unverified":5,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/attribute-based-representations-for-accurate","title":"An Attribute-based Method for Video Anomaly Detection","date":"2022-12-01","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spatio-temporal-predictive-tasks-for-abnormal","title":"Spatio-temporal predictive tasks for abnormal event detection in videos","date":"2022-10-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/context-recovery-and-knowledge-retrieval-a","title":"Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection","date":"2022-09-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/attention-based-residual-autoencoder-for","title":"Attention-based residual autoencoder for video anomaly detection","date":"2022-05-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fastano-fast-anomaly-detection-via-spatio","title":"FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation","date":"2021-06-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/anomaly-detection-in-video-via-self","title":"Anomaly Detection in Video via Self-Supervised and Multi-Task Learning","date":"2020-11-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-scene-agnostic-framework-with-adversarial","title":"A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video","date":"2020-08-27","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hamlet-a-hierarchical-multimodal-attention-1","title":"HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm","date":"2020-08-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/abnormal-event-detection-in-videos-using-1","title":"Abnormal Event Detection in Videos using Generative Adversarial Nets","date":"2017-08-31","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":20,"samples_ran":14,"samples_unverified":6,"pointer_only_for_licence":18,"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."}