ML Anthology

Finding Anomalies with Generative Adversarial Networks for a Patrolbot

Wallace E. Lawson, Esube Bekele, Keith Sullivan
CVPRW 2017 pp. 484-485
doi:10.1109/CVPRW.2017.68 /cvprw/2017/lawson2017cvprw-finding/

Abstract

We 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.

CVPRW Semantic Scholar

Cite

Text

Lawson et al. "Finding Anomalies with Generative Adversarial Networks for a Patrolbot." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.68

Markdown

[Lawson et al. "Finding Anomalies with Generative Adversarial Networks for a Patrolbot." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/lawson2017cvprw-finding/) doi:10.1109/CVPRW.2017.68

BibTeX

@inproceedings{lawson2017cvprw-finding,
  title     = {{Finding Anomalies with Generative Adversarial Networks for a Patrolbot}},
  author    = {Lawson, Wallace E. and Bekele, Esube and Sullivan, Keith},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {484-485},
  doi       = {10.1109/CVPRW.2017.68},
  url       = {https://mlanthology.org/cvprw/2017/lawson2017cvprw-finding/}
}