Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomalous entities detection using a cascade of deep learning models   Human actions that do not conform to usual behavior are considered as
anomalous and such actors are called anomalous entities. Detection of anomalous
entities using visual data is a challenging problem in computer vision. This
paper presents a new approach to detect anomalous entities in complex
situations of examination halls. The proposed method uses a cascade of deep
convolutional neural network models. In the first stage, we apply a pretrained
model of human pose estimation on frames of videos to extract key feature
points of body. Patches extracted from each key point are utilized in the
second stage to build a densely connected deep convolutional neural network
model for detecting anomalous entities. For experiments we collect a video
database of students undertaking examination in a hall. Our results show that
the proposed method can detect anomalous entities and warrant unusual behavior
with high accuracy.

3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph