An English video teaching classroom attention evaluation model incorporating multimodal information

In order to solve the problem of low detection efficiency and long working time in the traditional video surveillance system for abnormal behavior detection and identification methods. A multimodal abnormal behavior detection and identification method based on video surveillance is proposed and applied to an online video classroom concentration evaluation task for college students in English. The model works by capturing abnormal behaviors and facial expressions and building a joint network that fuses abnormal behaviors and facial expressions. By testing on two open-source datasets and self-built classroom real-time datasets, the results verify that the model in this paper has better recognition performance compared to current mainstream models while maintaining real-time performance. The model proposed in this paper provides a new way of thinking about building smart classrooms.