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Article

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

1
Computer Science, Université de Lille 3, 59655 Villeneuve-d’Ascq, France
2
Uncanny Vision Solutions, Bangalore, Karnataka 560008, India
*
Author to whom correspondence should be addressed.
Current address: 79 Rue Brillat Savarin, Paris 75013, France.
Received: 20 November 2017 / Revised: 29 January 2018 / Accepted: 1 February 2018 / Published: 7 February 2018
(This article belongs to the Special Issue Computer Vision and Pattern Recognition)
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. View Full-Text
Keywords: unsupervised methods; anomaly detection; representation learning; autoencoders; LSTMs; generative adversarial networks; Variational Autoencoders; predictive models unsupervised methods; anomaly detection; representation learning; autoencoders; LSTMs; generative adversarial networks; Variational Autoencoders; predictive models
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MDPI and ACS Style

Kiran, B.R.; Thomas, D.M.; Parakkal, R. An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos. J. Imaging 2018, 4, 36. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging4020036

AMA Style

Kiran BR, Thomas DM, Parakkal R. An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos. Journal of Imaging. 2018; 4(2):36. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging4020036

Chicago/Turabian Style

Kiran, B. R., Dilip M. Thomas, and Ranjith Parakkal. 2018. "An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos" Journal of Imaging 4, no. 2: 36. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging4020036

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