Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.

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Bibliographic Details
Title: Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.
Authors: Manasa P; Department of Computer Science and Engineering, Neil Gogte Institute of Technology, Hyderabad, India., Mahendar M; Department of Computer Science and Engineering, Neil Gogte Institute of Technology, Hyderabad, India., Narayanrao PV; Department of Computer Science and Engineering, Neil Gogte Institute of Technology, Hyderabad, India., Komuravelli S; Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Neil Gogte Institute of Technology, Hyderabad, India., Lakhani S; Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Neil Gogte Institute of Technology, Hyderabad, India., Basheer S; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia., Tabrez Quasim M; Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, P.O Box 551, Bisha, Saudi Arabia.
Source: Big data [Big Data] 2026 Jun 30, pp. 2167647X261463938. Date of Electronic Publication: 2026 Jun 30.
Publication Type: Journal Article
Journal Info: Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 101631218 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2167-647X (Electronic) Linking ISSN: 21676461 NLM ISO Abbreviation: Big Data Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2167-647X
DOI:10.1177/2167647X261463938