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Finding Anomalies With Generative Adversarial Networks for a Patrolbot
AbstractWe present an anomaly detection system based on an autonomous robot performing a patrol task. Using a generative adversarial network (GAN), we compare the robot's current view with a learned model of normality. Our preliminary experimental results show that the approach is well suited for anomaly detection, providing efficient results with a low false positive rate.
Related Material
[pdf][bibtex]@InProceedings{Lawson_2017_CVPR_Workshops,
author = {Lawson, Wallace and Bekele, Esube and Sullivan, Keith},
title = {Finding Anomalies With Generative Adversarial Networks for a Patrolbot},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
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