Region Based Anomaly Detection With Real-Time Training and Analysis

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

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

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019)
• 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: Jun 4, 2020
• 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
• Public URL: https://durham-repository.worktribe.com/output/1141378