Abnormal Event Detection From Videos Using a Two-Stream Recurrent Variational Autoencoder

With the massive deployment of distributed video surveillance systems, the automatic detection of abnormal events in video streams has become an urgent need. An abnormal event can be considered as a deviation from the regular scene; however, the distribution of normal and abnormal events is severely...

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Bibliographic Details
Published in:	IEEE transactions on cognitive and developmental systems Vol. 12; no. 1; pp. 30 - 42
Main Authors:	Yan, Shiyang, Smith, Jeremy S., Lu, Wenjin, Zhang, Bailing
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Abnormal event detection convolutional long-short term memory (LSTM) Event detection Feature extraction Hidden Markov models Image reconstruction Neural networks reconstruction error probability Semi-supervised learning Surveillance Surveillance systems Task analysis two-stream fusion variational autoencoder (VAE) Video data Videos
ISSN:	2379-8920, 2379-8939
Online Access:	Get full text
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