# Research on Students' Action Behavior Recognition Method Based on Classroom Time-Series Images
## 2 Related Work
### Behavior Recognition in Classroom Scenarios  
In recent years, there have been many scholars applying machine learning to students' action behavior recognition, which mainly focuses on recognizing students' action behaviors through data modalities such as human keypoints, still images, and videos. Therefore, this paper will introduce related research from these aspects. In the study based on human keypoints, [PERSON] et al. [20] proposed a method to recognize students' postures in the classroom, which used an improved HRNet network to extract students' keypoints and then used a support vector machine to classify students' classroom behaviors. [PERSON] et al. [21] extracted the key information of human skeleton from student behavioral images and recognized students' classroom behaviors based on a deep convolutional neural network (CNN-10). [PERSON] et al. [22] improved the traditional algorithm by combining the traditional cluster analysis algorithm and random forest algorithm with the human skeleton model to recognize students' classroom behaviors inreal-time. [PERSON] et al. [23] used OpenPose to extract human keypoints and used a graph convolutional neural network to classify the features and recognize the abnormal behaviors of students. In their study based on static images, [PERSON] et al. [24] proposed a motion object detection algorithm for student behavior recognition in the classroom, recognizing students' standing behaviors based on the region of interest (ROI) and face tracking, and students' hand-raising behaviors based on skin color detection. [PERSON] et al. [25] proposed an improved SSD object detection model to recognize the behavior of students and teachers in the teaching laboratory. [PERSON] et al. [26] improved the two-stage object detection network and proposed a new ROI extractor SAA module and a new detection head RST module for student behavior detection in classroom scenes. [PERSON] et al. [27] constructed a deep neural network to extract facial keypoints, recognize students' head postures and expressions, and classify classroom behaviors by combining the head gestures and expressions. [PERSON] et al. [28] improved the CBL module of the YOLOv5 model to detect students' classroom behavior from multiple perspectives to evaluate students' classroom attention. In the study based on videos, [PERSON] et al. [29] proposed a 3D multi-scale residual dense network based on heterogeneous view perception for recognizing students' classroom behaviors. [PERSON] et al. [30] combined a spatial affine transform network with a convolutional neural network to extract the spatiotemporal features of the video and fused the spatiotemporal features to classify students' behaviors by using a weighted sum method. Although the above studies have achieved good results in the field of student action behavior detection, there are still some shortcomings: (1) human keypoint-based studies have difficulties in extracting human keypoints in student-intensive classroom scenarios and it is difficult to accurately extract the keypoints of each student; (2) static-image-based studies ignore the temporal features of the action behaviors and do not incorporate time-series context information; (3) video-based studies incorporate the time-series context information of action behaviors but ignore the interaction between students and the context of the environment; these methods only recognize individual action behaviors and do not apply to real multi-person classroom scenarios.