Classroom behavior recognition driven by deep learning model

Classroom behavior of teachers and students is important for performance evaluation．Deepened integration of information technology and education necessitates updated classroom behavior analysis, particularly with introduction of artificial intelligence technology．Here computer vision technology was applied to identify behaviors in classroom videos．3D-CNN was used to identify teacher behaviors with dynamic characteristics．YOLO-v5 was used to identify student behaviors with multi-objectives．All data sets were collected and annotated．To verify effectiveness of model selected, comparative model performance was examined in the same data set．Our model showed good performance in classroom behavior recognition．Accurate identification of classroom behavior would help both teachers and students to understand classroom learning process and promote development of smart classroom．