  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.
