{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pheva-a-privacy-preserving-human-centric","title":"PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset","arxiv_id":"2408.14329","date":"2024-08-26","proceeding":null,"authors":["Ghazal Alinezhad Noghre","Shanle Yao","Armin Danesh Pazho","Babak Rahimi Ardabili","Vinit Katariya","Hamed Tabkhi"],"abstract":"PHEVA, a Privacy-preserving Human-centric Ethical Video Anomaly detection dataset. By removing pixel information and providing only de-identified human annotations, PHEVA safeguards personally identifiable information. The dataset includes seven indoor/outdoor scenes, featuring one novel, context-specific camera, and offers over 5x the pose-annotated frames compared to the largest previous dataset. This study benchmarks state-of-the-art methods on PHEVA using a comprehensive set of metrics, including the 10% Error Rate (10ER), a metric used for anomaly detection for the first time providing insights relevant to real-world deployment. As the first of its kind, PHEVA bridges the gap between conventional training and real-world deployment by introducing continual learning benchmarks, with models outperforming traditional methods in 82.14% of cases. The dataset is publicly available at https://github.com/TeCSAR-UNCC/PHEVA.git.","url_abs":"https://arxiv.org/abs/2408.14329v1","url_pdf":"https://arxiv.org/pdf/2408.14329v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pheva-a-privacy-preserving-human-centric","repo_url":"https://github.com/tecsar-uncc/pheva","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":null,"task_name":"Pose-based Anomaly Detection"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-pheva","task":"Anomaly Detection","dataset":"PHEVA","model":"MPED-RNN","rank_in_archive_order":1,"of":4,"metrics":{"AUC-ROC":"76.05"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-pheva","task":"Anomaly Detection","dataset":"PHEVA","model":"TSGAD (Pose Branch)","rank_in_archive_order":2,"of":4,"metrics":{"AUC-ROC":"68"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-pheva","task":"Anomaly Detection","dataset":"PHEVA","model":"GEPC","rank_in_archive_order":3,"of":4,"metrics":{"AUC-ROC":"62.25"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-pheva","task":"Anomaly Detection","dataset":"PHEVA","model":"STG-NF","rank_in_archive_order":4,"of":4,"metrics":{"AUC-ROC":"57.57"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}