Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

Abstract

The study of student behavior analysis in class plays a key role in teaching and educational reforms that can help the university to find an effective way to improve students' learning efficiency and innovation ability. It is also one of the effective ways to cultivate innovative talents. The traditional behavior recognition methods have many disadvantages, such as poor robustness and low efficiency. From a heterogeneous view perception point of view, it introduces the students' behavior recognition. Therefore, we propose a 3-D multiscale residual dense network from heterogeneous view perception for analysis of student behavior recognition in class. First, the proposed method adopts 3-D multiscale residual dense blocks as the basic module of the network, and the module extracts the hierarchical features of students' behavior through the densely connected convolutional layer. Second, the local dense feature of student behavior is to learn adaptively. Third, the residual connection module is used to improve the training efficiency. Finally, experimental results show that the proposed algorithm has good robustness and transfer learning ability compared with the state-of-the-art behavior recognition algorithms, and it can effectively handle multiple video behavior recognition tasks. The design of an intelligent human behavior recognition algorithm has great practical significance to analyze the learning and teaching of students in the class.