The study of students behavior analysis in class plays a key role in teaching and educational reforms which 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. Therefore, we propose a 3D multiscale residual dense network from heterogeneous view perception for analysis of students behavior recognition in class. First, the proposed method adopts 3D 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 students 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 to 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.

中文翻译：

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基于3D多尺度残差密集网络的课堂异构视知觉学生行为识别案例教学分析

学生课堂行为分析的研究在教学和教育改革中具有重要作用，可以帮助大学找到提高学生学习效率和创新能力的有效途径。也是培养创新人才的有效途径之一。传统的行为识别方法存在鲁棒性差、效率低等诸多缺点。因此，我们提出了一个来自异构视图感知的 3D 多尺度残差密集网络，用于分析学生在课堂上的行为识别。首先，所提出的方法采用3D多尺度残差密集块作为网络的基本模块，该模块通过密集连接的卷积层提取学生行为的层次特征。第二，学生行为的局部密集特征是适应性学习。第三，使用残差连接模块提高训练效率。最后，实验结果表明，与目前最先进的行为识别算法相比，所提出的算法具有良好的鲁棒性和迁移学习能力，能够有效处理多种视频行为识别任务。智能人体行为识别算法的设计对于分析班级学生的学与教具有重要的现实意义。并且可以有效处理多种视频行为识别任务。智能人体行为识别算法的设计对于分析班级学生的学与教具有重要的现实意义。并且可以有效处理多种视频行为识别任务。智能人体行为识别算法的设计对于分析班级学生的学与教具有重要的现实意义。