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Region based anomaly detection with real-time training and analysis.

Adey, P. and Bordewich, M. and Breckon, T.P. and Hamilton, O.K. (2019) 'Region based anomaly detection with real-time training and analysis.', in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019. Piscataway, NJ: IEEE, pp. 495-499.


We present a method of anomaly detection that is capable of real-time operation on a live stream of images. The real-time performance applies to the training of the algorithm as well as subsequent analysis, and is achieved by substituting the region proposal mechanism used in [9] with one that makes the overall method more efficient. where they generate thousands of regions per image, we generate far fewer but better targeted regions. We also propose a 'convolutional' variant which does away with region extraction altogether, and propose improvements to the density estimation phase used in both variants.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:21 September 2019
Date deposited:29 December 2019
Date of first online publication:17 February 2020
Date first made open access:04 June 2020

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