## 4 Experimental Results
### Experimental Setup
#### 4.1.2 Evaluation Metrics  
We tested our preliminary tracking module using the evaluation metrics proposed by GOT-10k and LaSOT. For GOT-10k, including mean average overlap (mAO) and mean success rate (mSR). For LaSOT, including area under the curve (AUC), normalized precision (PNorm), and precision (P). Three average precision (AP) and two mean average precision (mAP) in MS COCO 2017 were used to evaluate our object detection model.  
For the self-built dataset CLV, we defined three metrics to evaluate the performance of target re-identification:  
* \(ET\)  
The unit is \"times\", which represents the number of tracking errors using the SOT model in a video for tracking the target, mainly reflecting the stability of the model.  
\[ET=\sum_{k=1}^{n}1,\;\;Te_{k}\in TE \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video ID** & **Video Duration** & **Video Size** & **Frame Rate** & \
\begin{tabular}{c} **Number of Times the** \\ **Target Leaves the Shot** \\ \end{tabular} &
\begin{tabular}{c} **Total Time of the Target** \\ **Leaves the Shot** \\ \end{tabular} \\ \hline
001 & 40:20 & 1920 \(\times\) 1080 & 30 fps & 11 times & 79.674 s \\
002 & 50:35 & 1920 \(\times\) 1080 & 30 fps & 9 times & 179.614 s \\
003 & 37:42 & 1920 \(\times\) 1080 & 30 fps & 11 times & 106.721 s \\
004 & 33:51 & 1920 \(\times\) 1080 & 30 fps & 9 times & 142.153 s \\
005 & 43:35 & 1920 \(\times\) 1080 & 30 fps & 24 times & 332.490 s \\
006 & 45:14 & 1920 \(\times\) 1080 & 30 fps & 15 times & 163.069 s \\
007 & 32:04 & 1920 \(\times\) 1080 & 30 fps & 3 times & 110.833 s \\
008 & 41:40 & 1280 \(\times\) 960 & 30 fps & 8 times & 58.689 s \\
009 & 38:26 & 1920 \(\times\) 1080 & 30 fps & 7 times & 174.110 s \\
010 & 32:11 & 1920 \(\times\) 1080 & 30 fps & 14 times & 88.457 s \\
011 & 36:58 & 1280 \(\times\) 960 & 30 fps & 6 times & 101.776 s \\
012 & 40:46 & 1920 \(\times\) 1080 & 30 fps & 19 times & 254.330 s \\
013 & 30:09 & 1280 \(\times\) 960 & 30 fps & 4 times & 76.541 s \\
014 & 31:32 & 1920 \(\times\) 1080 & 30 fps & 13 times & 94.879 s \\
015 & 35:27 & 1920 \(\times\) 1080 & 30 fps & 16 times & 143.556 s \\
016 & 38:43 & 1920 \(\times\) 1080 & 30 fps & 12 times & 167.123 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of classroom video dataset CLV.  
where \(TE\) represents the total moments when tracking errors occur, and \(Te_{k}\) means the moment when a tracking error occurs during the \(k\)-th tracking error process.  
\(\bullet\quad ED\)  
The unit is \"second\", which represents the total duration of tracking errors by using the SOT model in a video for tracking the target, mainly reflecting the accuracy of the model.  
\[ED=\sum_{k=1}^{n}\left(Tc_{k}-Te_{k}\right) \tag{12}\]  
where \(Tc_{k}\) represents the moment when the tracking error was corrected during the \(k\)-th tracking error process.  
\(\bullet\quad TD\)  
The unit is \"second\", which represents the total duration of the time interval between the target's reappearance after leaving the shot and being tracked again by using the SOT model in a video, mainly reflecting the ability of target re-identification of the model.  
\[TD=\sum_{k=1}^{n}\left(Tr_{k}-Ta_{k}\right) \tag{13}\]  
where \(Tr_{k}\) and \(Ta_{k}\) represent the moment when the tracker re-identifies the target and the moment the target reappears in the shot during the \(k\)-th target re-identification process, respectively.