Vision transformer embedded video anomaly detection using attention driven recurrence

Vision transformer embedded video anomaly detection using attention driven recurrence

Automated video anomaly detection (VAD) is a challenging task due to its context-dependent and sporadic nature. However, recent deep learning advancements offer promising solutions. In this paper, we propose a novel framework for detecting anomalies in videos by uniquely analyzing spatial and tempor...

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Bibliographic Details
Main Authors: Ummay Maria Muna, Shanta Biswas, Syed Abu Ammar Muhammad Zarif, Philip Jefferson Deori, Tauseef Tajwar, Swakkhar Shatabda
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Array
Subjects:
Video anomaly detection
Deep learning
Vision transformer
Simple recurrent unit plus plus (SRU++)
Attention mechanism
Spatio-temporal feature analysis
Online Access:http://www.sciencedirect.com/science/article/pii/S2590005625000980
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http://www.sciencedirect.com/science/article/pii/S2590005625000980

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