# Track Line Recognition Based on Morphological Thinning Algorithm +
## 4 Experiments and Results
From the above, it can be concluded that our algorithm is better applicable to freight railways; however, in order to verify that this method is also applicable to other scenarios, we collected subway scene data which included videos of different directions of different driving routes. The resolution of each frame was also 1920 \(\times\) 1080, and the frame rate was 25 fps; we extracted the track region using our algorithm, as shown in Figure 19.  
We also chose video data from four different subway scenes and performed statistical analyses; the results are shown in Table 3. As can be seen in this table, we used the same conditions, retaining the images with a better processing effect and deleting the frames with discontinuities and noisy track lines, as shown by GNF. Table 4 shows that straight track line extraction accuracy achieves above 93%, curved track lines and multiple turnout line extraction also achieve more than 90% accuracy. Compared with freight railways in complex environments, subway lines are simple (no appearance of gravel roads, irregular \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**GNF**} \\ \cline{3-10}  & \multirow{2}{*}{**Scenes**} & \multirow{2}{*}{**TNF**} & \multicolumn{2}{c}{**Sobel**} & \multicolumn{2}{c}{**Skeleton**} & \multicolumn{2}{c}{**ZS**} & \multicolumn{2}{c}{**Ours**} \\ \cline{3-10}  & & & **Good Frames** & **Accuracy Rate** & **Good Frames** & **Accuracy Rate** & **Accuracy Rate** & **Good Frames** & **Accuracy Rate** \\ \hline
**Video 1** & rain, straights & 445 & 331 & 74.38\% & 399 & 89.66\% & 387 & 86.96\% & 410 & 90.11\% \\ \hline
**Video 2** & evening, & & & & & & & & & \\  & turnouts, curves & & & & & & & & & \\ \hline
**Video 3** & sunny, turnouts, & & & & & & & & & \\  & curves & & & & & & & & & \\  & curves & & & & & & & & & \\ \hline
**Video 4** &
\begin{tabular}{c} evening, \\ turnouts, \\ straights \\ \end{tabular} & 273 & 244 & 89.37\% & 236 & 86.44\% & 243 & 89.01\% & 245 & 89.74\% \\ \hline
**Video 5** &
\begin{tabular}{c} sunny, straights \\ turnouts, curves \\ \end{tabular} & 281 & 215 & 76.51\% & 251 & 89.32\% & 244 & 86.83\% & 262 & 93.23\% \\ \hline
**Video 6** &
\begin{tabular}{c} cloudy, \\ turnouts, curves \\ \end{tabular} & 479 & 435 & 90.81\% & 424 & 88.51\% & 438 & 91.44\% & 441 & 92.06\% \\ \hline
**Video 7** & rain, curves & 396 & 297 & 75.42\% & 340 & 86.03\% & 356 & 90.16\% & 364 & 92.02\% \\ \hline
**Video 8** &
\begin{tabular}{c} evening, \\ turnouts, \\ \end{tabular} & 366 & 289 & 79.01\% & 322 & 88.65\% & 318 & 87.24\% & 329 & 90.23\% \\ \hline
**Video 9** &
\begin{tabular}{c} sunny, straights \\ turnouts, \\ \end{tabular} & 421 & 374 & 89.24\% & 357 & 85.02\% & 378 & 90.16\% & 391 & 93.24\% \\ \hline
**Video 10** &
\begin{tabular}{c} evening \\ turnouts, curves \\ \end{tabular} & 356 & 306 & 86.21\% & 316 & 89.19\% & 320 & 90.16\% & 327 & 92.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the comparative experiment under freight railway scenario. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenes** & **Track Line Extraction Time (ms)** & **Track Region Extraction Time (ms)** & **Total Time (ms)** \\ \hline Simple track line (daytime) & 40.3 & 110.5 & 150.8 \\ Complex track line (daytime) & 44.6 & 120.3 & 164.9 \\ Simple track line & 41.2 & 108.4 & 149.6 \\ Complex track line & 46.0 & 120.0 & 166.0 \\ Multiple turnout track line (night) & 39.7 & 107.2 & 146.9 \\ Multiple turnout track line & 40.1 & 112.5 & 152.6 \\ (headlights at night) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the extraction time for track areas in different scenes under the condition of finite computational force. sleepers, road junctions or ravine fences); we can, therefore, conclude that the method is also applicable to subway railways.