基于生成对抗网络的巡逻机器人异常发现

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI:10.1109/CVPRW.2017.68

W. Lawson, Esube Bekele, Keith Sullivan

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引用次数: 37

摘要

我们提出了一种基于自主机器人执行巡逻任务的异常检测系统。使用生成对抗网络(GAN)，我们将机器人的当前视图与学习到的正态性模型进行比较。我们的初步实验结果表明，该方法非常适合于异常检测，提供了低误报率的高效结果。

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Finding Anomalies with Generative Adversarial Networks for a Patrolbot

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.

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来源期刊

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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