Visual anomaly detection under temporal and spatial non-uniformity for news finding robot

In this paper, we propose a news-gathering mobile robot system, and the novel visual anomaly detection method as the core function of news detection in the real world. Visual anomaly detection is important and widely applicable not only to the news-gathering robot but also to the security systems. However, visual anomaly detection from the mobile robot is highly challenging, because the appearances of images captured by the moving robot are dynamically changing. In consequence, the number of observed images at the same location becomes small, and the sampling interval of those images is not constant. To tackle this problem, we developed a new method to incorporate many samples observed at different locations as previous knowledge, which implicitly represent semantically similar to the intended location. Also, we developed a new statistical model, which explicitly considers sampling interval of input images, whereas conventional methods ignore correlation among samples. Experimental results demonstrate that our method outperforms conventional methods, and our mobile robot system including the proposed method finds, investigates, and publishes news of a local community of the real world.