# Research on Students' Action Behavior Recognition Method Based on Classroom Time-Series Images
## 4 Experimental Results and Analysis
### Student Action Behavior Detection Performance  
#### 4.3.1 Experimental Results and Analysis of the Student Action Behavior Dataset  
In the experiments on the student action behavior dataset, in this paper, the video frames are cropped to \(640\times 640\), the batch size is set to four, the initial learning rate is 0.00025, and the input of the network is 64 frames. Figure 9 shows the curves of the accuracy \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Pretrain** & **mAP\%** \\ \hline SlowFast, R101-NL & Kinetics-600 & 29.0 \\ X3D & Kinetics-600 & 27.4 \\ AIA & Kinetics-700 & 31.2 \\ ACAR & Kinetics-700 & 31.9 \\ MRSN & Kinetics-700 & 33.5 \\ DOAD & Kinetics-700 & 28.5 \\ HIT & Kinetics-700 & 32.6 \\ Ours & Kinetics-700 & 32.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with other models in the AVA dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Input** & **mAP\%** \\ \hline ACT & V & 69.5 \\ STEP & V + F & 75.0 \\ AIA & V & 81.7 \\ ACAR & V & 84.3 \\ MESN & V & 80.3 \\ DOAD & V & 74.8 \\ HIT & V & 84.8 \\ Ours & V & 82.2 \\ \hline \hline \end{tabular} \(\
abla\) and F in Table 2 denote visual frames and optical flow, respectively.  
\end{table} 
Table 2: Comparison with other models in the UCF101-24 dataset. and loss values of the proposed method during the experimental process. The results show that the improved AIA network in this paper converges stably during the training process and achieves 92% accuracy. To verify the effectiveness of the different modules added to the improved AIA network and the improved loss function, Table 3 lists the experimental results with the addition of the MsTA module and the MsCSA module, as well as the comparative results of replacing the loss function with the EFL function [12] and the EQLv2 function [42]. As can be seen from Table 3, after integrating the MsTA module and the MsCSA module into the AIA network, the mAP values of student action detection improved. Students' different action behaviors during class have different durations and visual speeds, and the MsTA module can extract action behavior features with different time scales. For example, when students are listening carefully, they usually look up and face the teacher, and there is not much posture change between the current frame and the history frame, however, when students are looking around, the module needs to combine the temporal features of multi-frames to recognize the student's \"looking around\" behavior. When students are reading, writing, or using mobile phones, they will turn the book, take a pen, or touch the book or mobile phone and the MsCSA module expands the receptive field in the space and enhances the response of the mobile phone, book, and other objects in the feature map, thus enhancing the interaction features of the student and the object so that the network better recognizes the interaction behaviors such as reading, writing, or using mobile phones. In addition, due to the problem of category imbalance in the dataset, there are fewer samples of behaviors such as lying on the table, using a mobile phone, and talking, and more samples of behaviors such as listening and reading. Both the EQLv2 function and the EFL function are used to improve the problem by adjusting the loss weights of the different categories. The experimental results in Table 3 show that the EFL function performs better in the study of this paper; so, the EFL is chosen as the improved AIA network's loss function. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision\%** & **Recall\%** & **mAP\%** \\ \hline AIA & 78.4 & 77.6 & 76.5 \\ AIA + MsTA & 81.5 & 78.3 & 79.4 \\ AIA + MsCSA & 79.2 & 78.5 & 77.8 \\ AIA + EQLv2 & 80.3 & 78.2 & 78.8 \\ AIA + EFL & 82.7 & 79.1 & 80.2 \\ AIA + MsTA + MsCSA + EQLv2 & 82.2 & 78.8 & 80.1 \\ AIA + MsTA + MsCSA + EFL (Ours) & 83.9 & 80.4 & 81.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of adding different modules and replacing loss functions.