Unsupervised anomalous event detection in videos using spatio-temporal inter-fused autoencoder

Automatic detection, localization and interpretation of an unusual event in a sequence of video is a challenging task due to its equivocal and complex nature. The development of deep neural networks have paved the way for more efficient recognition and analysis of anomalous events in video data. Wit...

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Vydané v:	Multimedia tools and applications Ročník 81; číslo 29; s. 42457 - 42482
Hlavní autori:	Aslam, Nazia, Kolekar, Maheshkumar H
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2022 Springer Nature B.V
Predmet:	1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System Artificial neural networks Computer architecture Computer Communication Networks Computer Science Data Structures and Information Theory Feature extraction Multimedia Information Systems Neural networks Special Purpose and Application-Based Systems Video data Video anomaly detection Spatio-temporal analysis Inter-fused autoencoder Unsupervised learning
ISSN:	1380-7501, 1573-7721
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