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 hu an activities by an autonomous mobile robot which is based on “Fast and Slow Thinking”. Our previous meth od employes deep captioning and detects anomalous image regions based on image visual features, cap tion features, and coordinate features. However, detecting anomalous image region pairs is a more challengin g problem due to the larger number of candidates. Moreover, realizing reminiscence, which represents re-ch ecking 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 anoma lies in real-time onboard an autonomous mobile robot. Our method consists of a fast module which models capt ion-coordinate features to detect single-region anomalies, and a slow module which models image visual featu r s and overlapping image regions to detect also neighboring-region anomalies. The reminiscence is tr igge ed by the fast module as a result of its anomaly detection and the slow module seeks for single-region anoma lies in recent images. Experiments with a real robot platform show the superiority of our method to the base line methods in terms of recall, precision, and

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