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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Bibliographic Details
Published in:	Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 1; pp. 1126 - 1143
Main Authors:	Asad, Mujtaba, Jiang, He, Yang, Jie, Tu, Enmei, Malik, Aftab Ahmad
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2022 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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