Detecting Anomalies From Human Activities By An Autonomous Mobile Robot Based On "Fast And Slow" Thinking

In this paper, we propose an anomaly detection method from human activities by an autonomous mobile robot which is based on "Fast and Slow Thinking". Our previous method employes deep captioning and detects anomalous image regions based on image visual features, caption features, and coordinate features. However, detecting anomalous image region pairs is a more challenging problem due to the larger number of candidates. Moreover, realizing reminiscence, which represents re-checking past, similar examples to cope with overlooking, is another challenge for a robot operating in real-time. Inspired by "Fast and Slow Thinking" from the dual process theory, we achieve detection of these kinds of anomalies in real-time onboard an autonomous mobile robot. Our method consists of a fast module which models caption-coordinate features to detect single-region anomalies, and a slow module which models image visual features and overlapping image regions to detect also neighboring-region anomalies. The reminiscence is triggered by the fast module as a result of its anomaly detection and the slow module seeks for single-region anomalies in recent images. Experiments with a real robot platform show the superiority of our method to the baseline methods in terms of recall, precision, and AUC.