# Research on Students' Action Behavior Recognition Method Based on Classroom Time-Series Images
## 1 Introduction  
Classroom teaching activities have always been the focus of research in the field of education, and students are the main body of the activities. Recognizing and analyzing students' action behavior plays an important role in teaching evaluation [1]. Student action behavior analysis can help teachers understand the learning process of students and is an important means of measuring learning effectiveness. Traditional methods of analyzing student action behaviors rely on manual assessment, manual recording, and manual coding to collect and interpret behavioral performance data, which have the disadvantages of strong coding subjectivity, small sample size, and high time-consumption [2]. With the development of education informatization, smart classrooms are widely used; so, cameras and other devices in the classroom can be used to obtain massive data such as videos and images in the teaching process. For the acquired massive data, selecting the appropriate sampling scheme and decomposition algorithm can effectively compress the data while retaining the integrity of the data features [3]. Moreover, extracting effective features from the data based on machine learning methods can be used to intelligently recognize students' action behaviors. In recent years, some researchers have used traditional algorithms such as wavelet transform and discrete path transform to extract features from data [4, 5]. Althoughthe extracted features are more representative and generalized, the traditional methods still have problems such as manual intervention and poor robustness. In addition, some researchers have also applied fractal theory to the field of image processing [6]. Different from traditional machine learning methods, deep learning automatically learns feature representations of data such as videos and images, which is robust and can be used as an effective tool for detecting students' action behaviors [7]. [PERSON] et al. [8] proposed a student posture detection method based on an improved Faster RCNN object detection model to recognize the behaviors of standing, sitting, and sleeping. [PERSON] et al. [9] proposed an action detection framework based on YOLOv5, which is used to detect the classroom behavior of students in the monitoring system under different backgrounds. Although the above studies achieved good results in student action behavior detection, they did not consider the temporal features of the behavior and the interaction with the spatial contextual environment, and there are limitations in understanding the action behavior based on a single-frame image. Different from student action behavior recognition based on static images, video behavior recognition can effectively utilize time-series information. [PERSON] et al. [10] proposed a college students' classroom behavior recognition algorithm based on spatiotemporal representation learning, which is used to recognize students' abnormal behaviors such as sleeping and playing mobile phones. However, this type of research is only applicable to action behavior recognition in single-person scenarios, which cannot mark the spatial location of each student and cannot be applied to action behavior detection in multi-person classroom scenarios. The purpose of spatiotemporal behavior detection is to locate each person in the video and recognize their action behaviors. Many existing studies have improved the performance of action behavior detection by modeling the interactions between the target person and the contextual environment. Students' action behaviors usually interact with other students, objects, etc., around them, and modeling these interactions can be used to recognize the students' action behaviors more effectively. In addition, the background of the classroom scene is complex, the students are crowded, the pixel area occupied by the front row and the back row students in the video is different, and the visual speed of each student's action behavior is also different; so, the extraction of video features needs to be optimized for these problems.