{"url":"/dataset/chad","name":"CHAD","full_name":"Charlotte Anomaly Dataset","description_markdown":"# CHAD: Charlotte Anomaly Dataset\r\n\r\nCHAD is high-resolution, multi-camera dataset for surveillance video anomaly detection. It includes bounding box, Re-ID, and pose annotations, as well as frame-level anomaly labels, dividing all frames into two groups of anomalous or normal. You can find the paper with all the details in the following link: [**CHAD: Charlotte Anomaly Dataset**](https://arxiv.org/abs/2212.09258 \"CHAD Paper\"). Please refer to the page of the dataset for more information.","description_withheld":null,"homepage":"https://github.com/TeCSAR-UNCC/CHAD","introduced_date":"2022-12-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/chad-charlotte-anomaly-dataset","title":"CHAD: Charlotte Anomaly Dataset","first_author":"Armin Danesh Pazho","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"RGB Video","url":"/datasets/modality/rgb-video"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Unsupervised Anomaly Detection","url":"/task/unsupervised-anomaly-detection","datasets_with_task":"/datasets/task/unsupervised-anomaly-detection"},{"name":"Video Anomaly Detection","url":"/task/video-anomaly-detection","datasets_with_task":"/datasets/task/video-anomaly-detection"},{"name":"Supervised Anomaly Detection","url":"/task/supervised-anomaly-detection","datasets_with_task":"/datasets/task/supervised-anomaly-detection"},{"name":"Group Anomaly Detection","url":"/task/group-anomaly-detection","datasets_with_task":"/datasets/task/group-anomaly-detection"}],"languages":[],"variants":["CHAD"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-anomaly-detection-on-chad","task":"Video Anomaly Detection","dataset_variant":"CHAD","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"PoseWatch-H","paper":"/paper/posewatch-a-transformer-based-architecture","metrics":{"AUC":"67.04"},"code_links":[{"title":"TeCSAR-UNCC/SPARTA","url":"https://github.com/TeCSAR-UNCC/SPARTA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/posewatch-a-transformer-based-architecture","title":"PoseWatch: A Transformer-based Architecture for Human-centric Video Anomaly Detection Using Spatio-temporal Pose Tokenization","date":"2024-08-27","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+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."}