## 4 Discussion
With respect to this, we can adapt Equation (1) so that the last  
\begin{table}
\begin{tabular}{c|c c c c|c c c c c|c} \hline \hline  & \multicolumn{6}{c}{**Real Scene 2**} & \multicolumn{6}{c}{**Synthetic Scene 2**} \\ \hline ID & 0 & 1 & 2 & 3 & AVG & 0 & 1 & 3 & 5 & AVG \\ \hline AOS & 0.97 & 0.97 & 0.98 & 0.90 & 0.96 & 0.97 & 0.94 & 0.91 & 0.95 & 0.94 \\ CER & 0.0012 & 0.0027 & 0.0022 & 0.0035 & 0.0024 & 0.0025 & 0.0031 & 0.0038 & 0.0021 & 0.0029 \\ TL & 0.97 & 0.98 & 0.99 & 0.98 & 0.98 & 0.99 & 0.  
bin of \(j\) contains the results for all IoU values ranging from 0.85 to 0.95. By this modified measure, our detector achieves a mAP of 99.2 %. In conclusion, we showed in this experiment that computer generated data is capable of successfully augmenting a construction site related dataset for the purpose of training a CNN. The reasonable choice of synthetic training samples can considerably increase the detection quality. These findings correspond to those in other application areas. It shows that such data is not only advantageous for well structured scenarios like car parks, but also yields reasonable results in crucially  
Figure 6: Comparing the detection results of YOLO trained on different datasets. Red rectangles display YOLO’s detections whereas green rectangles indicate manually labeled ground truth. (**a,c,e**) show the results of a detector trained on our real world dataset only. The right column illustrates the detection improvements for the same image sections if particular synthetic data is additionally provided for training. In (**b**), the general accuracy is improved by extending the training dataset by similar data. In (**d**), detections in under- and overexposed areas are improved by adding samples with different lighting conditions. In (**f**), workers can be detected despite slight occlusions and dramatic overexposure after adding a large amount of data samples. more complex environments like construction sites. This is also confirmed by the tracking results on the real world scenes of the second experiment. The simple tracking system based on a detector which was trained on only sparse real world data augmented with synthetic samples already enables suitable monitoring. This further substantiates the use of synthetic data for the training of CNNs in the context of construction sites. Beyond these findings, the second experiment emphasizes the comparability of real world and synthetic scenes in terms of evaluating vision-based detectors and trackers. The comparison of the tracking results given in Tables 2 and 3 reveals that our tracker acts similar on both kinds of data. On both scenes, the accuracy measured by the AOS and CER on the synthetic sequences is slightly lower than these on real world sequences. This is not necessarily due to the nature of synthetic data but rather associated with the more precise ground truth labels on the synthetic dataset resulting from the automatic labeling method. These labels enclose the workers even closer than those in the manually labeled sequences so that minor deviations during detection result in lower AOS and CER. The comparison of the green ground truth rectangles in Figure 5a,b as well as in Figure 5c,d illustrates this graphically. Nevertheless, on average there is only a deviation of about four percentage points in AOS and a deviation of 0.0005 in CER on the first scene. Similarly, low deviations are given for the second scene with two percentage points in AOS and 0.0005 in CER. These results indicate a reasonable comparability of the tracking performance on real world and synthetic scenarios. Very similar track lengths on both scenes additionally confirm this finding. The comparison has shown that the evaluation results on real world and computer generated video sequences resemble each other closely. Accordingly, the quality of a tracking system can be deduced on the basis of computer generated video sequences if sufficient real world data cannot be acquired.