Deep anomaly detection through visual attention in surveillance videos

Deep anomaly detection through visual attention in surveillance videos

Abstract This paper describes a method for learning anomaly behavior in the video by finding an attention region from spatiotemporal information, in contrast to the full-frame learning. In our proposed method, a robust background subtraction (BG) for extracting motion, indicating the location of att...

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Bibliographic Details
Main Authors: Nasaruddin Nasaruddin, Kahlil Muchtar, Afdhal Afdhal, Alvin Prayuda Juniarta Dwiyantoro
Format: Article
Language:English
Published: SpringerOpen 2020-10-01
Series:Journal of Big Data
Subjects:
Visual attention approach
Convolutional neural network (CNN)
Integrated surveillance system
Anomaly classification
Online Access:http://link.springer.com/article/10.1186/s40537-020-00365-y

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http://link.springer.com/article/10.1186/s40537-020-00365-y

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