Visual anomaly detection from small samples for mobile robots

Hiroharu Kato, T. Harada, Y. Kuniyoshi
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引用次数: 5

Abstract

We propose a novel method of visual anomaly detection for mobile robots in daily real-life settings. Visual anomaly detection using mobile robots is important for security systems or simply for gathering information. However, this task is challenging for two reasons. First, because the number of observed images sampled at the same location is small, anomaly detection systems cannot use standard statistical methods. Second, anomalies must be detected in the presence of other continuous, ambient changes in the visual scene, such as changes in lighting from morning to night. Regarding the former problem, we develop and apply an analysis-by-synthesis-based anomaly detection method for mobile robots. For the latter, we propose a novel definition of anomaly that uses observed samples at other locations to filter out ambient changes that should be ignored by the system. Experimental results demonstrate that our method can detect anomalies from small samples in the presence of ambient changes, which could not be detected by conventional methods.
移动机器人小样本视觉异常检测
我们提出了一种在日常生活环境中移动机器人视觉异常检测的新方法。使用移动机器人进行视觉异常检测对于安全系统或简单地收集信息非常重要。然而,由于两个原因,这项任务具有挑战性。首先,由于在同一位置采样的观测图像数量很少,异常检测系统无法使用标准的统计方法。其次,必须在视觉场景中存在其他连续的环境变化时检测异常,例如从早到晚的照明变化。针对前一个问题,我们开发并应用了一种基于综合分析的移动机器人异常检测方法。对于后者,我们提出了一种新的异常定义,该定义使用在其他位置观察到的样本来过滤掉应该被系统忽略的环境变化。实验结果表明,该方法可以在环境变化的情况下检测到常规方法无法检测到的小样本异常。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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