Anomalous Human Action 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 actions using visual data is a challenging problem in computer vision. This paper presents a new approach to detect anomalous actions 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 actions. For experiments we collect a video database of students undertaking examination in a hall. Our results show that the proposed method can detect anomalous actions and warrant unusual behavior with high accuracy.