Residual Spatiotemporal Autoencoder with Skip Connected and Memory Guided Network for Detecting Video Anomalies

Real-time video anomaly detection and localization still prevail as a challenging task. Autoencoders are expected to give high reconstruction error for abnormal events than normal events while trained on video segments of normal events. Nevertheless, this assumption is not always true in practice. S...

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Veröffentlicht in:	Neural processing letters Jg. 53; H. 6; S. 4677 - 4692
Hauptverfasser:	Chandrakala, S., Srinivas, V., Deepak, K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2021 Springer Nature B.V
Schlagworte:	Anomalies Artificial Intelligence Classification Complex Systems Computational Intelligence Computer Science Deep learning Localization Neural networks Performance degradation Video anomaly detection Residual blocks Normality modeling Memory guided network Spatio temporal autoencoders
ISSN:	1370-4621, 1573-773X
Online-Zugang:	Volltext
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