Abnormal behavior monitoring enhanced smart university stadium under the background of “Internet plus”

Abstract

With the rapid development of the Internet of Things and 5G technology, smart university gymnasiums have become more and more important. However, it has become increasingly difficult for university gymnasium management, especially to detect abnormal behavior with dense crowds under limited venue space. To handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) abnormal behavior detection system which consists of the 5G camera, 5G transmission network and cloud platform. The 5G camera captures and transmits the video to the cloud platform by exploiting the 5G wireless sensor network. In the cloud platform, a hybrid variational autoencoder backbone which exploits the pre‐trained VGG16 and Transformer model is deployed to detect abnormal behaviors. Moreover, by introducing adversarial training mechanisms, the robustness of the proposed model is effectively improved. The experimental results on our self‐built gymnasium abnormal behavior dataset show that the proposed model can correctly identify most of the abnormal behaviors in the gymnasium compared to other models.