Research on behavior recognition algorithms in classroom scenarios

The quality of teaching has always been a primary concern in the field of education. With the introduction of the smart classroom concept and advancements in hardware and software technologies, there is a need for an automatic and unbiased tool to evaluate teaching quality. Based on the principle of computer vision, this paper designs a behavior recognition network to classify students' behaviors in class. These classification results can provide data basis for evaluating the concentration of students and the attractiveness of teachers in the classroom. Also this information is essential for teachers to analyze their courses, optimize course rhythm, and improve teaching quality.