## 2 Background
### Student Behavior Recognition  
Human behavior recognition has attracted considerable research attention in the field of computer vision, especially in the classroom environment. Human behavior recognition is a challenging task [11]. Under the influence of various factors, such as different illumination, complex background, multi-perspectives, and large intra-class differences, human behavior recognition algorithms are mainly divided into two types: (1) based on traditional machine learning and (2) based on deep learning. Each method has its advantages and disadvantages [12]. The key to an action recognition algorithm based on traditional machine learning is feature extraction. It usually takes a lot of effort to design features that meet requirements and simply implement them. The main factors that affect video presentation include the complexity of the background, the change of human behavior speed, and the change of various perspectives. A convolutional neural network (CNN), with its strong scalability and end-to-end learning and training framework, has made breakthroughs in many fields, such as semantic segmentation, behavior recognition, and action classification. Action recognition based on the CNN framework can be divided into two types: one is a single-stream CNN framework, which uses only one stream (time or space) at a time. It is a dual-stream CNN framework, which uses both time stream and space stream. For the single-stream CNN framework, from the perspective of spatial CNN, a single video frame is input for behavior recognition, but it lacks the consistency of front and back actions. In terms of time CNN structure, human behavior information is usually obtained from dense optical flow. This kind of intensive time sampling leads to the high computational cost of video, and only a fixed number of video frames can be extracted from the video every once in a while. For the dual-stream CNN framework, the spatio-temporal information is integrated, and the recognition result is better than that of the single-stream CNN framework, such as three-dimensional convolution dual-stream neural network and track pool fusion dual-stream CNN network. [PERSON] et al. [12] first proposed the concept of three-dimensional convolution, and used the three-dimensional convolution kernel to extract temporal and spatial features for behavior recognition [13]. A dual-stream convolutional neural network for behavior recognition is proposed, which is divided into a spatial stream convolutional network and a time stream convolutional network. The spatial convolution network takes a frame of an RGB image as input, which represents the apparent static information at a certain moment in the video. The time stream convolution network takes stacked optical stream images as input. The time flow convolution network takes several consecutive optical flow images as input to represent the motion information of objects. Finally, the classification results of the two networks are fused to obtain the final accuracy. This model breaks the intensive trajectory extraction algorithm in the field of improved behavior recognition. [PERSON] et al. [14] proposed a new three-dimensional convolutional neural network (C3D). Three-dimensional continuous video frames are stacked in the network as the network input, a 3D convolution kernel is used to convolve the stacked cubes, and its time dimension is larger than that of a 2D convolution kernel. In this way, the motion information can be obtained from successive frames. The biggest advantage of this algorithm is that its recognition speed is greatly improved compared with the two-stream algorithm. In 2020, aiming at the complex problem of student behavior recognition in video, [PERSON] et al. [15] proposed a new feature fusion network for educating students' behavior recognition. The new feature fusion network includes two main stages: feature extraction and classification. They combine spatial affine transformation networks and convolutional neural networks to extract more detailed features.