## 4 Results
### Tracking
#### 4.2.1 Tracking Results Using a Classical Machine Learning Detector  
We applied the tracking system based on the classical machine learning detector to all three evaluation scenes. To each resulting track, a random ID was assigned. Further, we determined the AOS, CER and TL for each track separately. The performance of this system on each of the three evaluation scenes is summarized in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ID** & **AOS** & **CER** & **TL** \\ \hline \multirow{4}{*}{Scene 1} & 0 & 0.92 & 0.002 & 0.97 \\  & 5 & 0.65 & 0.004 & 0.92 \\  & 8 & 0.90 & 0.002 & 0.99 \\  & 9 & 0.87 & 0.002 & 1 \\ \hline \multirow{4}{*}{Scene 2} & 1 & 0.69 & 0.004 & 0.92 \\  & 2 & 0.88 & 0.002 & 0.95 \\  & 3 & 0.79 & 0.003 & 0.97 \\  & 4 & 0.88 & 0.002 & 0.88 \\ \hline Scene 3 & 0 & 0.82 & 0.004 & 1 \\ \hline Average & & 0.82 & 0.003 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the classical machine learning tracking system applied to all three evaluation scenes. For each scene the resulting measures according to Equations (2)–(4) are given. Tracking IDs are assigned randomly to each particular track. The last row highlights the performance of this system averaged over all scenes. The results indicate a significant decrease in tracking quality for overexposed scenes. This becomes clear when comparing the results of Track 5 with those of the other tracks of the first scene. As shown in Figure 8, Track 5 is located in a highly overexposed section of the scene, whereas the other tracks traverse well-illuminated sections only. This complicates the detection and the tracking consequently results in an inaccurate location, as illustrated in Figure 8a. This figure shows the AOS measure by comparing ground truth data in green to the actual tracks in red and the CER measure by blue lines indicating the distance between the labels' centers. As can be seen, Tracks 0 and 8 in the well-illuminated area match the ground truth closely and their centers are also close to each other. In contrast, the overlapping area of Track 5 in the highly overexposed section and its corresponding ground truth label is substantially smaller, whereas their centers' distance is considerably large. Such deviations during the tracking impair its accuracy which becomes visible from the jagged walking path determined for Track 5 in Figure 8b. This is also supported by the TL of only 0.92. Workers in overexposed areas are harder to detect so that the tracking begins delayed, which results in a shortened track length. In Scene 2, these effects are much less pronounced since the scene is only slightly overexposed. As the comparison of the AOS shows, the difference in the labels' overlap of Scenes 1 and 2 is only 0.025 on average, and, even if the outlier (Track 5) in Scene 1 is disregarded, the difference raises to only about 0.086. The average TL in this scene also decreases only moderately compared to Scene 1. By depicting an example of Scene 2, Figure 9a illustrates that the quality of our tracking approach is sufficient even on slightly overexposed scenes. This finding is supported by measuring the TL and the CER. Both do not vary significantly from Scene 1 to Scene 2. All construction workers are consistently tracked almost throughout the entire duration of both scenes. By applying the tracking system to Scene 3, we show its behavior in the case of moving objects which are not pedestrian construction workers. The results for Scene 3 in Table 2 depict that only one track is set up. As graphically confirmed by Figure 9b, this track belongs to the only worker in this scene. The worker is tracked successfully and no further tracks for the non-worker objects are mistakenly generated. Again, the AOS achieved by the classical system is within an acceptable range, and CER and TL confirm that the worker is tracked consistently throughout the whole scene's duration. Figure 8: Example of the tracking performance of the classical approach on Scene 1. (**a**) Illustration of the AOS and CER measures.