## 3 Materials and Methods
### Encoding Method
#### 3.1.2 Physical Posture Characteristics of Classroom S-T Behavior
As teachers' behavior is closely related to verbal information, when image information is used alone for discrimination, there will be many ambiguous judgments. For example, we can not describe a teacher's \"Explanation behavior\" with actions. To improve this problem, this paper adopts a hierarchical recognition method of posture-action behavior. We recognize teachers' actions by their local postures instead of directly recognizing their behaviors because gestures and actions have a unique correspondence, while actions and behaviors have a \"many-to-many\" relationship. The recognition of teachers' behaviors can be achieved by combining action recognition and situational reasoning. Figure 2 is the teacher's behavior posture image. The following is the coding table of teachers' actions. As can be seen from Table 4, it describes teachers' posture in more detail and divides the head (left and right directions) and body into three categories. Positive represents facing the students (P), back represents facing the blackboard (B), and side represents the state between them (S); Head (up and down direction) describes the pitching state of the head, which can be divided into three categories (up (U), down (D), and flat (F)). The hands are divided into two categories (yes, no); yes means that the palm is over the waist, or the arm has a large bending; and no means \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Behavior**} & \multicolumn{4}{c}{**Local Attitude Description**} & \multirow{2}{*}{**Identified**} \\ \cline{2-2} \cline{4-5}  & **Head** & **Hand** & **Body** & \\ \hline \multirow{2}{*}{Listening} & Raise & \multirow{2}{*}{Turn} & \multirow{2}{*}{Flat} & \multirow{2}{*}{Sit} & \multirow{2}{*}{Yes} \\  & Turn & & & \\ \hline Speaking & Raise & Null & **Stand** & Yes \\ \hline Raising hands & Raise & Raise & Sit & Yes \\ \hline Discussion & **Turn** & Null & Sit & Yes \\ \hline Writing & **Lower** & Flat & Sit & Yes \\ \hline \multirow{2}{*}{Reading} & \multirow{2}{*}{Raise} & \multirow{2}{*}{Flat} & **Sit (covered with books)** & \multirow{2}{*}{Yes} \\  & & & & **with books)** & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coding table of students’ behavior of human body local posture based on RGB images. Figure 2: Teachers’ actions and gestures. the opposite. Compared with students' behavior, teachers' actions need the joint expression of various local gestures, and it is difficult to rely on a single gesture for expression. At the same time, because there is no feature design for the teacher's mouth shape when judging the behavior of the explanation, we only rely on the behavior of explanation (with the arm), and the behavior of explanation without gestures can not be identified. This is the drawback of the lack of information channels. (II) Bone information  
Human skeleton information is obtained by posture estimation technology, and this paper uses Openpose [24] as a tool. Human skeleton information refers to the coordinate information of human joints, which is concise, so it is often used in human behavior recognition. Similarly, because there is less information when it is missing, it will have a greater impact on the recognition effect. Therefore, considering the serious occlusion between students, the small target in the back row, and the serious lack of skeletal information, this paper only designs the skeletal features of teachers' behavior. A human body skeleton diagram is shown in Figure 3.  
The teacher's local posture and action are the two targets we recognize, so the bone features are designed to correspond to the local posture features of RGB images, as shown in Table 5.