{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:31:39Z","timestamp":1762432299266,"version":"3.37.3"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Due to its widespread application in the field of public security, anomaly detection in crowd scenes has recently become a hot topic. Some deep learning-based methods led to significant accomplishments in this field. Nevertheless, due to the scarcity of data and the misclassification of queries which most of them suffer to some extent from a sudden and infrequent overfitting. Though, we tried to solve the above problems, understand the long video streams and establish an accurate and reliable security system in order to improve its performance in detecting anomalies. We also referred to the hash technique, which has proven to be the most efficient method used when researching about large-scale image recovery. Thus, this article offers a smart video anomaly detection solution. In this paper, we combine the advantages of both deep hashing and deep auto-encoders to show that tracking changes in deep hash components across time and can be used to detect local anomalies. More precisely, we start with a new technique to minimize the mass of input data and information in order to decrease the time of calculation using a new dynamic frame skipping technique. Then, we propose to measure local anomalies by combining semantic with low-level optical flows to balance the performance and perceptibility. The experimental results illustrate that the proposed methods surpass these baselines for the detection and localization of anomalies.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac152","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T09:51:43Z","timestamp":1668592303000},"page":"3-17","source":"Crossref","is-referenced-by-count":1,"title":["Deep Hashing and Sparse Representation of Abnormal Events Detection"],"prefix":"10.1093","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2357-6817","authenticated-orcid":false,"given":"Mariem","family":"Gnouma","sequence":"first","affiliation":[{"name":"University of Gabes Research Team on Intelligent Machines, National Engineering School of Gabes, , Street Omar Ibn El Khattab, Zrig Eddakhlania, Gabes 6029 , Tunisia"},{"name":"Departement of Computer Sciences, Faculty of Sciences of Gabes , Erriadh City, Zrig, Gabes 6072 , Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8148-1621","authenticated-orcid":false,"given":"Ridha","family":"Ejbali","sequence":"additional","affiliation":[{"name":"University of Gabes Research Team on Intelligent Machines, National Engineering School of Gabes, , Street Omar Ibn El Khattab, Zrig Eddakhlania, Gabes 6029 , Tunisia"},{"name":"Departement of Computer Sciences, Faculty of Sciences of Gabes , Erriadh City, Zrig, Gabes 6072 , Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4013-5834","authenticated-orcid":false,"given":"Mourad","family":"Zaied","sequence":"additional","affiliation":[{"name":"University of Gabes Research Team on Intelligent Machines, National Engineering School of Gabes, , Street Omar Ibn El Khattab, Zrig Eddakhlania, Gabes 6029 , Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"2024012011403583500_ref1","first-page":"1","volume-title":"Proceedings of Visual Communications and Image Processing (VCIP","author":"Jiang","year":"2013"},{"key":"2024012011403583500_ref2","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/978-3-030-15628-2_15","article-title":"Video analytics for visual surveillance and applications: an overview and survey","volume-title":"Machine Learning Paradigms","author":"Olatunji","year":"2019"},{"key":"2024012011403583500_ref3","first-page":"1","volume-title":"Proceedings of the 8th\u00a0ACM international conference on PErvasive technologies related to assistive environments.","author":"Zhang","year":"2015"},{"key":"2024012011403583500_ref4","first-page":"1","volume-title":"ACM Computing Surveys (CSUR)","author":"Pang","year":"2021"},{"key":"2024012011403583500_ref5","doi-asserted-by":"crossref","first-page":"104078","DOI":"10.1016\/j.imavis.2020.104078","article-title":"A comprehensive review on deep learning-based methods for video anomaly detection","volume":"106","author":"Nayak","year":"2021","journal-title":"Image Vis. 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