{"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/abnormal-event-detection-in-videos-using-1","title":"Abnormal Event Detection in Videos using Generative Adversarial Nets","arxiv_id":"1708.09644","date":"2017-08-31","proceeding":null,"authors":["Mahdyar Ravanbakhsh","Moin Nabi","Enver Sangineto","Lucio Marcenaro","Carlo Regazzoni","Nicu Sebe"],"abstract":"In this paper we address the abnormality detection problem in crowded scenes.\nWe propose to use Generative Adversarial Nets (GANs), which are trained using\nnormal frames and corresponding optical-flow images in order to learn an\ninternal representation of the scene normality. Since our GANs are trained with\nonly normal data, they are not able to generate abnormal events. At testing\ntime the real data are compared with both the appearance and the motion\nrepresentations reconstructed by our GANs and abnormal areas are detected by\ncomputing local differences. Experimental results on challenging abnormality\ndetection datasets show the superiority of the proposed method compared to the\nstate of the art in both frame-level and pixel-level abnormality detection\ntasks.","url_abs":"http://arxiv.org/abs/1708.09644v1","url_pdf":"http://arxiv.org/pdf/1708.09644v1.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":[],"tasks":[{"task_slug":"abnormal-event-detection-in-video","task_name":"Abnormal Event Detection In Video"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"semi-supervised-anomaly-detection","task_name":"Semi-supervised Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/abnormal-event-detection-in-video-on-ubi","task":"Abnormal Event Detection In Video","dataset":"UBI-Fights","model":"Adversarial Generator","rank_in_archive_order":5,"of":6,"metrics":{"AUC":"0.533","Decidability":"0.147","EER":"0.484"},"uses_additional_data":false},{"leaderboard":"/sota/abnormal-event-detection-in-video-on-ucsd","task":"Abnormal Event Detection In Video","dataset":"UCSD Ped2","model":"Adversarial Generator","rank_in_archive_order":4,"of":4,"metrics":{"AUC":"97.4%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-anomaly-detection-on-ubi","task":"Semi-supervised Anomaly Detection","dataset":"UBI-Fights","model":"Adversarial Generator","rank_in_archive_order":6,"of":7,"metrics":{"AUC":"0.533","Decidability":"0.147","EER":"0.484"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.09644","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}