# Student Behavior Recognition in Classroom Based on Deep Learning
## 2 Related Work
### Methods Based on Deep Learning  
In recent years, the rapid development of deep learning has significantly transformed the field of behavior detection, particularly through the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Deep learning models automatically learn and extract features from data via multi-layer neural network structures, allowing them to excel in complex scenarios. CNNs are proficient at extracting spatial features from image data, while RNNs can capture temporal dynamics changes in videos. The integration of these two models provides powerful tools for behavior detection [12].  
[PERSON] et al. [13] proposed a Two-Stream Network for behavior recognition that integrates optical flow and RGB information between frames simultaneously, thereby enhancing the detection performance. To overcome the limitation that CNN models can only process 2D inputs, [PERSON] et al. [14] developed an innovative 3D CNN action recognition model. This model extracts features from both spatial and temporal dimensions through 3D convolutions. The final feature output integrates information across all channels, ultimately achieving strong recognition performance. [PERSON] et al. [15] introduced a simple yet effective end-to-end Semantic Guided Neural Network (SGN) for skeleton-based action recognition. Their research incorporated high-level semantic information related to skeletons into a neural network, highlighting the importance of semantic information. They also developed a robust baseline that enhanced the performance while reducing the model size. [PERSON] et al. [16] proposed the T-TINY-YOLO network model, optimized from YOLO, for detecting abnormal behavior targets. They addressed the issue of numerous zero-weight parameters in YOLO by presenting a CNN pruning scheme and implementing real-time optimization. [PERSON] et al. [17] utilized convolutional neural networks to extract features from depth images and pose data. These action features, along with three convolutional neural network channels, were used for maximal feature extraction to recognize human actions. [PERSON] et al. [18] proposed an intelligent system based on facial expression recognition, which utilizes a CNN to classify students' facial expressions. The system automatically detects and analyzes these expressions to assess their engagement levels. [PERSON] et al. [19] introduced the OpenPose network based on Part Affinity Fields for real-time multi-person pose estimation. This network can simultaneously detect body keypoints of multiple individuals and addresses the challenges of pose recognition in multi-person interaction environments.  
Table 1 compares different detection methods in the relevant literature with the model in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** & **Detection Scenario** & **Innovation** \\ \hline [PERSON] et al. [13] & Two-Stream Network & Human behavior in simple backgrounds & Fusion of optical flow and RGB information \\ [PERSON] et al. [14] & 3D CNN & Action recognition in complex scenes & Extraction of spatial and temporal features \\ [PERSON] et al. [15] & Skeleton-Based Action & Multi-person interaction & Introduction of high-level semantic information \\ [PERSON] et al. [16] & T-TINY-YOLO & Abnormal behavior detection & CNN pruning and real-time optimization \\  & & & & Integration of YOLOv5, CA mechanism, and OpenPose, improved small-object detection and pose recognition \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of model methods.  
However, current research seldom focuses on the dynamic characteristics of students' behavior in classroom environments, particularly in complex and changing scenarios. To improve the robustness and reliability of behavior detection algorithms, it is essential to optimize feature extraction and fusion methods. This optimization will enhance the accuracy and efficiency of the model in processing student behavior.