Visual anomaly detection from small samples for mobile robots

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.