{"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/stemgan-spatio-temporal-generative","title":"STemGAN: spatio-temporal generative adversarial network for video anomaly detection","arxiv_id":null,"date":"2023-09-22","proceeding":"Applied Intelligence 2023 9","authors":["Rituraj Singh","Krishanu Saini","Anikeit Sethi","Sumeet Saurav","Aruna Tiwari","Sanjay Singh"],"abstract":"Automatic detection and interpretation of abnormal events have become crucial tasks in large-scale video surveillance systems. The challenges arise from the lack of a clear definition of abnormality, which restricts the usage of supervised methods. To this end, we propose a novel unsupervised anomaly detection method, Spatio-Temporal Generative Adversarial Network (STemGAN). This framework consists of a generator and discriminator that learns from the video context, utilizing both spatial and temporal information to predict future frames. The generator follows an Autoencoder (AE) architecture, having a dual-stream encoder for extracting appearance and motion information, and a decoder having a Channel Attention (CA) module to focus on dynamic foreground features. In addition, we provide a transfer-learning method that enhances the generalizability of STemGAN. We use benchmark Anomaly Detection (AD) datasets to compare the performance of our approach with the existing state-of-the-art approaches using standard evaluation metrics, i.e., AUC (Area Under Curve) and EER (Equal Error Rate). The empirical results show that our proposed STemGAN outperforms the existing state-of-the-art methods achieving an AUC score of 97.5% on UCSDPed2, 86.0% on CUHK Avenue, 90.4% on Subway-entrance, and 95.2% on Subway-exit.","url_abs":"https://doi.org/10.1007/s10489-023-04940-7","url_pdf":"https://doi.org/10.1007/s10489-023-04940-7","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":[],"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":null,"task_name":"Generative Adversarial Network"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly Detection"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-chuk-avenue","task":"Anomaly Detection","dataset":"CUHK Avenue","model":"STemGAN","rank_in_archive_order":29,"of":35,"metrics":{"AUC":"86.0"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-ucsd-ped2","task":"Anomaly Detection","dataset":"UCSD Ped2","model":"STemGAN","rank_in_archive_order":9,"of":14,"metrics":{"AUC":"97.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}