# Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling

arXiv: https://arxiv.org/abs/1509.07979 · HTML: https://mlsanity.com/paper/1509.07979/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection](https://mlsanity.com/paper/2207.02281/) (2022)

## Abstract (arXiv)

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.