Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling
This addresses the problem of identifying anomalies in unstructured environments like underwater or dynamic scenes for robotics and monitoring applications, but it appears incremental as it adapts an existing technique to new data.
The paper tackles anomaly detection in video data by applying a Bayesian non-parametric topic modeling technique, achieving results such as detecting all three instances of an underwater vehicle in coral reef footage.
This paper explores the use of a Bayesian non-parametric topic modeling technique for the purpose of anomaly detection in video data. We present results from two experiments. The first experiment shows that the proposed technique is automatically able characterize the underlying terrain, and detect anomalous flora in image data collected by an underwater robot. The second experiment shows that the same technique can be used on images from a static camera in a dynamic unstructured environment. In the second dataset, consisting of video data from a static seafloor camera capturing images of a busy coral reef, the proposed technique was able to detect all three instances of an underwater vehicle passing in front of the camera, amongst many other observations of fishes, debris, lighting changes due to surface waves, and benthic flora.