An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anoma...

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Bibliographic Details
Published inJournal of imaging Vol. 4; no. 2; p. 36
Main Authors Kiran, B., Thomas, Dilip, Parakkal, Ranjith
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2018
Subjects
Annotations
Anomalies
anomaly detection
Artificial intelligence
autoencoders
Cases (containers)
Data mining
Datasets
Deep learning
generative adversarial networks
International conferences
LSTMs
Neural networks
Pattern recognition
Pedestrians
predictive models
representation learning
State-of-the-art reviews
unsupervised methods
Variational Autoencoders
France
United States > US
Online AccessGet full text
ISSN2313-433X
2313-433X
DOI10.3390/jimaging4020036

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Summary:Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
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ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging4020036