{"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/a-scene-agnostic-framework-with-adversarial","title":"A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video","arxiv_id":"2008.12328","date":"2020-08-27","proceeding":null,"authors":["Mariana-Iuliana Georgescu","Radu Tudor Ionescu","Fahad Shahbaz Khan","Marius Popescu","Mubarak Shah"],"abstract":"Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. The complexity of the task arises from the commonly-adopted definition of an abnormal event, that is, a rarely occurring event that typically depends on the surrounding context. Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events. Our framework is composed of an object detector, a set of appearance and motion auto-encoders, and a set of classifiers. Since our framework only looks at object detections, it can be applied to different scenes, provided that normal events are defined identically across scenes and that the single main factor of variation is the background. To overcome the lack of abnormal data during training, we propose an adversarial learning strategy for the auto-encoders. We create a scene-agnostic set of out-of-domain pseudo-abnormal examples, which are correctly reconstructed by the auto-encoders before applying gradient ascent on the pseudo-abnormal examples. We further utilize the pseudo-abnormal examples to serve as abnormal examples when training appearance-based and motion-based binary classifiers to discriminate between normal and abnormal latent features and reconstructions. We compare our framework with the state-of-the-art methods on four benchmark data sets, using various evaluation metrics. Compared to existing methods, the empirical results indicate that our approach achieves favorable performance on all data sets. In addition, we provide region-based and track-based annotations for two large-scale abnormal event detection data sets from the literature, namely ShanghaiTech and Subway.","url_abs":"https://arxiv.org/abs/2008.12328v5","url_pdf":"https://arxiv.org/pdf/2008.12328v5.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":"a-scene-agnostic-framework-with-adversarial","repo_url":"https://github.com/lilygeorgescu/AED","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-scene-agnostic-framework-with-adversarial","repo_url":"https://github.com/m-3lab/awesome-visual-sensory-anomaly-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"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":"anomaly-detection-in-surveillance-videos","task_name":"Anomaly Detection In Surveillance Videos"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/abnormal-event-detection-in-video-on-ucsd","task":"Abnormal Event Detection In Video","dataset":"UCSD Ped2","model":"Background-Agnostic Framework","rank_in_archive_order":2,"of":4,"metrics":{"AUC":"98.7%"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-chuk-avenue","task":"Anomaly Detection","dataset":"CUHK Avenue","model":"Background-Agnostic Framework","rank_in_archive_order":11,"of":35,"metrics":{"AUC":"92.3%","FPS":"25","RBDC":"65.05","TBDC":"66.85"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-shanghaitech","task":"Anomaly Detection","dataset":"ShanghaiTech","model":"Background-Agnostic Framework","rank_in_archive_order":15,"of":31,"metrics":{"AUC":"82.7%"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-ubnormal","task":"Anomaly Detection","dataset":"UBnormal","model":"Background-Agnostic Framework","rank_in_archive_order":12,"of":14,"metrics":{"AUC":"61.3%","RBDC":"25.43","TBDC":"56.27"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-ucsd-ped2","task":"Anomaly Detection","dataset":"UCSD Ped2","model":"Background-Agnostic","rank_in_archive_order":4,"of":14,"metrics":{"AUC":"98.7%","FPS":"24"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-ucsd-peds2","task":"Anomaly Detection","dataset":"UCSD Peds2","model":"Background-Agnostic Framework","rank_in_archive_order":1,"of":3,"metrics":{"AUC":"98.7"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on-3","task":"Anomaly Detection In Surveillance Videos","dataset":"UCSD Peds2","model":"Background-Agnostic Framework","rank_in_archive_order":1,"of":6,"metrics":{"AUC":"98.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2008.12328","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.12328"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lilygeorgescu/AED","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/m-3lab/awesome-visual-sensory-anomaly-detection","reach":{"status":"ok"}}],"summary":{"ran_fixture":1,"ran_draft_wrong":1,"ran_honours":1},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"a2c564ef3e28ab1d","entry":"bb_intersection_over_union","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"a2c564ef3e28ab1d"}},{"code_sha256_prefix":"32f9a43a8db49a6e","entry":"compute_tbdr","repo":"lilygeorgescu/AED","repo_kind":"official","path":"evaluation/compute_tbdc_rbdc.py","file_url":"https://github.com/lilygeorgescu/AED/blob/HEAD/evaluation/compute_tbdc_rbdc.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"32f9a43a8db49a6e"}},{"code_sha256_prefix":"331b15f4d9630ab7","entry":"get_matching_gt_indices","repo":"lilygeorgescu/AED","repo_kind":"official","path":"evaluation/compute_tbdc_rbdc.py","file_url":"https://github.com/lilygeorgescu/AED/blob/HEAD/evaluation/compute_tbdc_rbdc.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"331b15f4d9630ab7"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}