## 5 Results  
Table 9 summarizes the best results of each approach, and Figure 10 shows the results for each class of these feature sets. Each of these plots show a similar outline, with pedestrian as the worst recognized class and cargo bike as the best one, except for the contour features, where cargo bike was the second-worst class. The general distribution of the results can partially be explained by the size of the vehicle compared with the size of the driver and the variety of vehicles. We only had a single cargo bike, and it was the largest vehicle in our test set-up. Changing the driver did not influence the overall appearance or silhouette of the object as much as in other classes. This is further supported by Figure 11, which shows that the cargo bike class was the only class with no significant amount of unknown classifications. The bike, as the second-largest vehicle, had the second-highest score on average. Even if the contrast did not always allows seeing the bike in the thermal image, the vehicle forced the driver into a certain position, which could be distinguished. The same applied to the scooter; all scooters forced the driver into a certain position. Pedestrians that were not constrained by a vehicle had the largest variety in posture and were therefore the hardest to distinguish.  
In addition, further inspection of the results shows that the errors in the classification of the cargo bike could also be attributed to partially inadequate labeling (see Figure 12). The manual process allowed for labeling images in the context of the previous and following images, which in turn allowed for the labeling of small object segments. These rare and adverse occurrences led to errors in classification.  
Figure 11 shows the confusion matrices of the results for the RBF mode of the initial tests with a scaled ROI and the final test with a scaled ROI combined with the contour moments. On the left axis, the expected label is shown, and on the bottom axis, the  
Figure 9: Training behavior of the NM500 with increased number of images in the training dataset, with feature extraction using scaled region of interest of 16 \(\times\) 16 pixels. (**a**) Neurons committed per class. (**b**) Classification accuracy in KNN mode. (**c**) Classification accuracy in RBF mode.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Scaled ROI**} & \multicolumn{2}{c}{**Histogram**} & \multicolumn{2}{c}{**Properties**} & \multicolumn{2}{c}{**Moments**} & \multicolumn{2}{c}{**Combination**} \\ \cline{2-11}  & **RBF** & **KNN** & **RBF** & **KNN** & **RBF** & **KNN** & **RBF** & **KNN** & **RBF** & **KNN** \\ \hline Median & 84 & 84 & 59 & 66 & 68 & 70 & 76 & 77 & 87 & 87 \\ Sigma & 1.0 & 1.4 & 1.5 & 1.7 & 1.3 & 1.2 & 1.4 & 1.7 & 0.9 & 1.1 \\ \hline \end{tabular}
\end{table}
Table 9: Best F1 score results of the tested feature extraction methods.  
predicted label is shown. The \"0\" row and column show the results if no decision could be made. The first row is empty because the datasets did not contain any examples for no classes. The confusion matrices show that the change in the aspect ratio and the addition of moments had no adverse effects, the classification accuracy for each class went up, and a wrong detection did not shift classes.  
Figure 11: Row-wise normalized RBF confusion matrices of (**a**) 16 \(\times\) 16 scaled ROI and (**b**) 12 \(\times\) 18 combined scaled ROI and contour moments, with true labels on the \(y\) axis and predicted labels on the \(x\) axis.  
Figure 12: Examples for adverse labeling, the red box marks the ROI of the label object.  
Figure 10: Best results for the tested feature extraction methods: (**a**) scaled ROI, (**b**) contour features, (**c**) contour moments, and (**d**) combined moments and scaled ROI.