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Because of the\nlack of prior information regarding abnormal events, these methods are not\nfully-equipped to differentiate between normal and abnormal events. In this\nwork, we formalize abnormal event detection as a one-versus-rest binary\nclassification problem. Our contribution is two-fold. First, we introduce an\nunsupervised feature learning framework based on object-centric convolutional\nauto-encoders to encode both motion and appearance information. Second, we\npropose a supervised classification approach based on clustering the training\nsamples into normality clusters. A one-versus-rest abnormal event classifier is\nthen employed to separate each normality cluster from the rest. For the purpose\nof training the classifier, the other clusters act as dummy anomalies. During\ninference, an object is labeled as abnormal if the highest classification score\nassigned by the one-versus-rest classifiers is negative. Comprehensive\nexperiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and\nUMN. Our approach provides superior results on all four data sets. On the\nlarge-scale ShanghaiTech data set, our method provides an absolute gain of 8.4%\nin terms of frame-level AUC compared to the state-of-the-art method [Sultani et\nal., CVPR 2018].","url_abs":"http://arxiv.org/abs/1812.04960v2","url_pdf":"http://arxiv.org/pdf/1812.04960v2.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":"object-centric-auto-encoders-and-dummy","repo_url":"https://github.com/fjchange/object_centric_VAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"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":"binary-classification","task_name":"Binary Classification"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-chuk-avenue","task":"Anomaly Detection","dataset":"CUHK Avenue","model":"Object-centric AE","rank_in_archive_order":22,"of":35,"metrics":{"AUC":"87.4%","RBDC":"15.77","TBDC":"27.01"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-shanghaitech","task":"Anomaly Detection","dataset":"ShanghaiTech","model":"Object-centric AE","rank_in_archive_order":20,"of":31,"metrics":{"AUC":"78.7%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1812.04960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.04960"}},"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. 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