An Intelligent System for Detecting Abnormal Behavior in Students Based on the Human Skeleton and Deep Learning

With the use of an intelligent video system, this research provides a method for detecting abnormal behavior based on the human skeleton and deep learning. To begin with, the spatiotemporal features of human bones are extracted through iterative training using the OpenPose deep learning network and the redundant information of human bone facial features is reduced in the feature extraction process, effectively reducing the time it takes to identify and analyze abnormal behavior. The collected human skeleton features are then classified using a graph convolution neural network to reduce the computational complexity of the behavior identification algorithm, and the sliding window voting method is used to further improve the accuracy of the behavior classification in practical application, resulting in the diagnosis and classification of abnormal behavior of students under video surveillance. Finally, using the self-built student trajectory data set and the INRIA data set, simulation analysis is performed, and the practicality and superiority of the proposed method for abnormal behavior detection is confirmed by comparing it to the existing abnormal behavior recognition methods. The proposed method for detecting anomalous behavior in a self-built database and INRIA data set has a high accuracy of more than 99.50 percent and a high processing efficiency rate.