An optimized multi-scale convolutional autoencoder for efficient abnormal event detection using rgb, depth and optical flow data

In this study, we propose a novel framework for detecting abnormal events in surveillance videos, a critical yet challenging task in security applications. This research introduces a robust and efficient solution for video anomaly detection, offering substantial improvements in surveillance systems&...

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Bibliographic Details
Published in:	Multimedia tools and applications Vol. 84; no. 28; pp. 34401 - 34435
Main Author:	Alqahtani, Abdullah
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.08.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Anomalies Artificial neural networks Aspect ratio Automation Cameras Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Deep learning Effectiveness Machine learning Multimedia Information Systems Object recognition Optical flow (image analysis) Optimization Public safety Public spaces Security Smart cities Special Purpose and Application-Based Systems Surveillance Surveillance systems Video data Deep learning Optimization algorithm Object detection Video anomaly detection Feature fusion Key frame extraction Abnormal event detection
ISSN:	1573-7721, 1380-7501, 1573-7721
Online Access:	Get full text
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