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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Bibliografski detalji
Glavni autori: Nasaruddin Nasaruddin, Kahlil Muchtar, Afdhal Afdhal, Alvin Prayuda Juniarta Dwiyantoro
Format: Članak
Jezik:English
Izdano: SpringerOpen 2020-10-01
Serija:Journal of Big Data
Teme:
Visual attention approach
Convolutional neural network (CNN)
Integrated surveillance system
Anomaly classification
Online pristup:http://link.springer.com/article/10.1186/s40537-020-00365-y

Internet

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

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