Multi-Stream 3D latent feature clustering for abnormality detection in videos

Detection of abnormal behavior in surveillance videos is essential for public safety and monitoring. However, it needs constant human focus and attention for human-based surveillance systems, which is a challenging process. Therefore, automatic detection of such events is of great significance. Abno...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 52; číslo 1; s. 1126 - 1143
Hlavní autori:	Asad, Mujtaba, Jiang, He, Yang, Jie, Tu, Enmei, Malik, Aftab Ahmad
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.01.2022 Springer Nature B.V
Predmet:	Artificial Intelligence Clustering Computer Science Feature extraction Machines Manufacturing Mechanical Engineering Normality Processes Public safety Surveillance Surveillance systems Training Video Multi-stream 3D features Feature clustering Autonomous video surveillance Anomaly detection 3D convolutional autoencoders
ISSN:	0924-669X, 1573-7497
On-line prístup:	Získať plný text
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