## 2 Related Work
### Behavior Detection in Classroom Scenarios  
In a related study of behavior detection in classroom scenes, the authors of [23] used a matrix of pressure sensors mounted on seats and the backs of chairs to collect students' various postures. The extracted posture features are fed into a feedforward neural network for training, and nine kinds of postures are classified with high precision. The authors of [24] used Hexiware to collect students' heartbeat data and categorize them by the K-Means algorithm as a measure of students' learning status in the classroom. The authors of [25] proposed a student body gesture recognition method based on the Fisher Broad learning system, and defined seven learning behaviors, which achieved good results on the self-built dataset. The authors of [26] used OpenPose framework to collect students' skeleton information, build a neural network to classify the extracted skeletal data by normalizing joint position, joint distance, and skeletal angle, and propose a student behavior recognition system based on human skeleton estimation and person detection. The authors of [27] built a deep convolutional neural network to identify head poses, used cascaded facial feature point positioning to extract facial expression key points, and distinguished students' classroom behaviors by combining head poses and facial expressions. The authors of [28] proposed a face tracking algorithm based on area of interest to detect students' standing behavior in the classroom, and also developed an algorithm based on skin color detection to recognize students' hand behavior. The authors of [29] introduced a cascaded RFB module in the YOLOv3 algorithm, which improves the feature extraction capability of the original network and realizes the goal of identifying small and medium-sized target students in the classroom. At the same time, the SE attention mechanism was introduced to express feature information in a finer-grained manner.  
Through the above research and analysis, we can find that there are still some shortcomings in classroom behavior detection. (1) Although the behavior detection method using hardware has a high recognition rate, it needs to use hardware for multi-modal data collection, which is difficult to be applied to real classroom scenarios and is not suitable for large-scale popularization. (2) When using deep learning methods in the classroom, some algorithms cannot detect multiple objects in the same image frame at the same time and are not suitable for classroom scenarios with multiple students; or, although multiple people can be detected in the same frame, the algorithm lacks real-time performance. There is still a lack of a real-time classroom behavior detection methods. (3) The lack of classroom behavior datasets: there are currently no public large-scale classroom scene datasets, which makes it difficult for researchers to train in deep learning.  
To address the above points, this paper marks the datasets of classroom behavior detection on real class scenarios and optimizes the behavior detection model according to the difficulties in class scenarios, and the optimized algorithm was tested to achieve good results.