Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomaly Detection in Unstructured Environments using Bayesian
  Nonparametric Scene Modeling   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.

26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc