{"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/attention-based-residual-autoencoder-for","title":"Attention-based residual autoencoder for video anomaly detection","arxiv_id":null,"date":"2022-05-25","proceeding":"Applied Intelligence 2022 5","authors":["Viet-Tuan Le","Yong-Guk Kim"],"abstract":"Automatic anomaly detection is a crucial task in video surveillance system intensively used for public safety and others. The present system adopts a spatial branch and a temporal branch in a unified network that exploits both spatial and temporal information effectively. The network has a residual autoencoder architecture, consisting of a deep convolutional neural network-based encoder and a multi-stage channel attention-based decoder, trained in an unsupervised manner. The temporal shift method is used for exploiting the temporal feature, whereas the contextual dependency is extracted by channel attention modules. System performance is evaluated using three standard benchmark datasets. Result suggests that our network outperforms the state-of-the-art methods, achieving 97.4% for UCSD Ped2, 86.7% for CUHK Avenue, and 73.6% for ShanghaiTech dataset in term of Area Under Curve, respectively.","url_abs":"https://link.springer.com/article/10.1007/s10489-022-03613-1","url_pdf":"https://link.springer.com/content/pdf/10.1007/s10489-022-03613-1.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":"attention-based-residual-autoencoder-for","repo_url":"https://github.com/vt-le/astnet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"anomaly-detection-in-surveillance-videos","task_name":"Anomaly Detection In Surveillance Videos"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly Detection"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-chuk-avenue","task":"Anomaly Detection","dataset":"CUHK Avenue","model":"ASTNet","rank_in_archive_order":26,"of":35,"metrics":{"AUC":"86.7%"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-shanghaitech","task":"Anomaly Detection","dataset":"ShanghaiTech","model":"ASTNet","rank_in_archive_order":26,"of":31,"metrics":{"AUC":"73.6"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-ucsd-ped2","task":"Anomaly Detection","dataset":"UCSD Ped2","model":"ASTNet","rank_in_archive_order":10,"of":14,"metrics":{"AUC":"97.4%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}