## 6 Experimental Results and Analysis
### Experimental Results of the Collection of KMNs  
KMNs are collected using the preprocessing module. The collection is evaluated according to the results of the above experiments. We tested all 42 video sequences from the dataset comprising video sequences. The ground truth of each KMN in the testing \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nodes** & \(num\) & \(IDSW\) & \(MOTA\) & \(MOTP\) \\ \hline _Off-block 1_ & 323 & 3 & 94.25\% & 90.56\% \\ _Off-block 2_ & 323 & 13 & 89.79\% & 91.25\% \\ _In-block 1_ & 292 & 0 & 96.61\% & 95.63\% \\ _In-block 2_ & 324 & 12 & 92.35\% & 94.56\% \\ _Docking stairs 1_ & 239 & 0 & 97.34\% & 94.4\% \\ _Docking stairs 2_ & 874 & 0 & 99.54\% & 91.3\% \\ _Undocking stairs 1_ & 252 & 5 & 96.52\% & 91.23\% \\ _Undocking stairs 2_ & 421 & 0 & 94.29\% & 92.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Association results of the quantitative assessment for eight video sequences. videos is manually annotated by a professional from the airport surface service department. The frame error (\(FE\)) and corresponding time error (\(TE\)) are used to quantitatively evaluate the performance:  
\[F\text{E}=\frac{\sum\limits_{i=1}^{L}\left|N_{i}-N_{i}^{gt}\right|}{L},T\text{E} =0.2\times F\text{E}, \tag{17}\]  
where \(N_{i}\) is the frame index obtained by the proposed algorithm and \(N_{i}^{gt}\) is the corresponding ground truth. \(L\) is the number of test sequences. The frequency of the video sequence captured in the dataset is 5 fps, and the time interval between the current frame and the previous is thus 0.2 s. The (empirical) parameter settings are \(r=5\), \(Th_{C}=5\), and \(Th_{IoU}=0.1\) for the aircraft and \(Th_{tc}=3\) and \(Th_{IoU}=0.05\) for the mobile aircraft landing stairs. Results of quantitative evaluation are listed in Table 6. Figure 11a,b show the results of the collection of the four types of KMN at aircraft stands 939 and 734, respectively. In the figure, each time axis represents a time series in terms of the number of frame. The time axis is labeled with the origin and the total number of frames, and the predicted nodes of activity occurrence are shown as red dots, the actual nodes of activity occurrence are shown as blue dots, and the white dots are the process nodes of activity occurrence. The following observations are made from the aforementioned results. First, the error is largest for the _in-block_ node. This is because aircraft have a larger bounding box compared with vehicles, making it more difficult to determine changes in aircraft movement. Second, the error at position 734 is slightly higher than that at position 939. This is due to more interference around position 734, which is farther away from the camera, and less interference around  
Figure 10: Association results of four video sequences. position 939, where the positioning information is more accurate. Third, the timing error in the automatic collection of KMNs is well below 60 s, as required by the A-CDM system. To sum up, since all of these test samples were from the 734 and 939 aircraft positions at Beijing Capital Airport, we believe that the proposed framework can be applied directly to KMN collection of these two aircraft stands. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position No.** & **Nodes** & **\(L\)** & **FE(_Unit: Frame_)** & **TE(_Unit: Second_)** \\ \hline \multirow{4}{*}{No.939} & _In-block_ & 2 & 51 & 10.2 \\  & _Off-block_ & 3 & 9 & 1.8 \\  & _Docking stairs_ & 4 & 27 & 5.4 \\  & _Undocking stairs_ & 1 & 29 & 5.8 \\ \hline \multirow{4}{*}{No.734} & _In-block_ & 14 & 63 & 12.6 \\  & _Off-block_ & 13 & 8 & 1.6 \\ \cline{1-1}  & _Docking stairs_ & 3 & 46 & 9.2 \\ \cline{1-1}  & _Undocking stairs_ & 2 & 36 & 7.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative evaluation of the automatic collection of KMNs. Figure 11: Results of the automatic collection of four KMNs.