## 4 Results
### Classroom S-T Behavior Recognition and Analysis
#### 4.2.2 Training and Test Results
This shows that our S-T behavior judgment rule does reflect the actual situation to some extent and improves the recognition accuracy, but it can not completely correct the recognition errors. We randomly selected 10 classes for testing, and the average accuracy rate of 10 classes reached 77.2%. In addition, combined with S-T analysis method, we divide classroom types into lecture type, practice type, conversational type, and mixed type so as to explore the influence of machine recognition results on S-T analysis. As shown in Table 10, we find that the correct rate of class type judgment is 80%, of which 125 and 149 are wrong in class prediction because of the low accuracy of S-T behavior recognition. We also selected a class with correct and wrong predictions to draw S-T curves, as shown in Figure 9. The accuracy of S-T behavior recognition in Class 16 is higher, and the S-T curve is closer to the real curve. Figure 8: Detection and recognition in real scenes ((1–6) show the recognition results of different images). However, the students in Class 149 are more densely distributed, the camera resolution is lower, and the camera position is lower, which makes the overlap between students more serious. These reasons make the recognition rate in Class 149 much lower than that in Class 116. Combined with the actual situation of the classroom with wrong prediction, the reasons for the wrong classroom behavior recognition are summarized as follows:  
* The irregular posture of students affects the behavior recognition, such as the confusion between holding chin and raising hands, and the confusion between whispering and discussion;
* For the time being, the intelligent recognition method in this paper can not effectively distinguish the categories with similar characteristics, such as the difference of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class No.** & **Accuracy Rate** & **Actual Classroom Type** & **Forecast Classroom Type** \\ \hline
32 & 75.80\% & Mixed type & Mixed type \\ \hline
35 & 71.10\% & Conversational type & Conversational type \\ \hline
48 & 76.60\% & Conversational type & Conversational type \\ \hline
102 & 79.10\% & Mixed type & Mixed type \\ \hline
109 & 80.50\% & Conversational type & Conversational type \\ \hline
116 & 85.70\% & Mixed type & Mixed type \\ \hline
125 & 66.70\% & Mixed type & Conversational type \\ \hline
141 & 80.00\% & Practice type & Practice type \\ \hline
148 & 85\% & Practice type & Practice type \\ \hline
149 & 71.70\% & Mixed type & Conversational type \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Actual test table of the algorithm. Figure 9: S–T curves of two classes. head angles between writing and listening, and the characteristics of students' pen holding in the writing behavior of images are too small;
* The image quality is not high, and the behavior is blocked. We obviously find that the recognition accuracy of the students sitting in the front row is higher than that in the back row. On the one hand, the students in the back row are blocked seriously; on the other hand, the students in the front row occupy more pixels in the image, so the display effect is better. To sum up, the method in this paper has a better recognition effect in the classroom with fewer students, standardized students' posture, and better camera imaging quality.