{"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/detecting-abnormal-events-in-video-using","title":"Detecting abnormal events in video using Narrowed Normality Clusters","arxiv_id":"1801.05030","date":"2018-01-12","proceeding":null,"authors":["Radu Tudor Ionescu","Sorina Smeureanu","Marius Popescu","Bogdan Alexe"],"abstract":"We formulate the abnormal event detection problem as an outlier detection\ntask and we propose a two-stage algorithm based on k-means clustering and\none-class Support Vector Machines (SVM) to eliminate outliers. In the feature\nextraction stage, we propose to augment spatio-temporal cubes with deep\nappearance features extracted from the last convolutional layer of a\npre-trained neural network. After extracting motion and appearance features\nfrom the training video containing only normal events, we apply k-means\nclustering to find clusters representing different types of normal motion and\nappearance features. In the first stage, we consider that clusters with fewer\nsamples (with respect to a given threshold) contain mostly outliers, and we\neliminate these clusters altogether. In the second stage, we shrink the borders\nof the remaining clusters by training a one-class SVM model on each cluster. To\ndetected abnormal events in the test video, we analyze each test sample and\nconsider its maximum normality score provided by the trained one-class SVM\nmodels, based on the intuition that a test sample can belong to only one\ncluster of normality. If the test sample does not fit well in any narrowed\nnormality cluster, then it is labeled as abnormal. We compare our method with\nseveral state-of-the-art methods on three benchmark data sets. The empirical\nresults indicate that our abnormal event detection framework can achieve better\nresults in most cases, while processing the test video in real-time at 24\nframes per second on a single CPU.","url_abs":"http://arxiv.org/abs/1801.05030v4","url_pdf":"http://arxiv.org/pdf/1801.05030v4.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":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":null,"task_name":"CPU"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[{"method_slug":"svm","method_name":"SVM"},{"method_slug":"k-means-clustering","method_name":"k-Means Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-chuk-avenue","task":"Anomaly Detection","dataset":"CUHK Avenue","model":"Narrowed Normality Clusters","rank_in_archive_order":20,"of":35,"metrics":{"AUC":"88.9%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.05030","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}