Students’ Classroom Behavior Recognition Based on Behavior Pose and Attention Mechanism

Artificial intelligence technology drives the reform of traditional teaching concepts, models, content, and methods, providing assistance for the informatization and intelligence of education. Classroom teaching activities have always been a focus of research in the field of education. In complex classroom behavior recognition scenarios, it is significant to recognizing students' classroom behavior by using computer vision techniques. However, the prevailing behavior recognition methods currently demonstrate poor performance in classroom behavior recognition. Consequently, this article introduces a college student classroom behavior recognition model that integrates behavior pose and attention mechanisms to enhance the accuracy of recognizing student classroom behavior in high school classrooms. To validate the effectiveness of the proposed model, experiments were conducted using a labeled high school classroom behavior dataset. The experimental results show that the proposed model performs exceptionally well in recognizing college students' classroom behavior.