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Region Based Anomaly Detection With Real-Time Training and Analysis

Adey, P.; Bordewich, M.; Breckon, T.P.; Hamilton, O.K.

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Authors

P. Adey

M. Bordewich

O.K. Hamilton



Abstract

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.

Citation

Adey, P., Bordewich, M., Breckon, T., & Hamilton, O. (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 (495-499). https://doi.org/10.1109/icmla.2019.00092

Conference Name 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019)
Conference Location Boca Raton, Florida, USA
Start Date Dec 16, 2019
End Date Dec 19, 2019
Acceptance Date Sep 21, 2019
Online Publication Date Feb 17, 2020
Publication Date 2019
Deposit Date Dec 20, 2019
Publicly Available Date Mar 28, 2024
Publisher Institute of Electrical and Electronics Engineers
Pages 495-499
Book Title 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019
DOI https://doi.org/10.1109/icmla.2019.00092

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Accepted Conference Proceeding (816 Kb)
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