Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling

TL;DR

This paper introduces a Bayesian non-parametric scene modeling approach for anomaly detection in unstructured environments, demonstrated through underwater and coral reef video data, effectively identifying unusual objects and events.

Contribution

The paper presents a novel Bayesian non-parametric technique for anomaly detection in unstructured environments, capable of characterizing terrain and detecting anomalies in diverse video data.

Findings

Successfully detected underwater vehicles and flora anomalies

Effectively characterized terrain in underwater environments

Identified anomalies amidst environmental variability

Abstract

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.