## 5 Conclusions  
Productivity and safety management on construction sites could benefit from monitoring pedestrian workers. Based on recorded walking trajectories of workers provided by a tracking system, the workers' current paths could be assessed and optimized to solve certain tasks more efficiently. These trajectories also reveal the workers' attention with respect to hazards like falling edges or hazardous materials and areas. Owing to this, safety trainings could be tailored explicitly to the needs of the workers. If the localization of workers is conducted live, workers and machine operators could even be warned of looming hazardous situations, which enables them to counteract early. Computer vision methods are more suitable for such a precise tracking of workers all over the site due to various shortcomings arising from the radio-frequency technology used by tag-based alternatives. However, computer vision approaches have to be trained and evaluated on a vast amount of images. Appropriate datasets are rarely publicly available and recording a sufficient amount of data is extremely time-consuming. For this reason, we investigated the use of synthetic data, which is generated from 3D environments. Besides a small real world dataset, we generated a synthetic dataset that covers diverse environmental and illumination conditions. In order to analyze the usability of data generated from 3D scenarios, we built a simple tracking system. This consists of a YOLOv3 detector identifying pedestrian workers and a Kalman filter tracking those workers in video sequences. In our experiments, we examined the suitability of synthetic data individually for training and evaluation purposes of a computer vision tracking system. First, we iteratively added more and more synthetic data samples \begin{table}
\begin{tabular}{c|c c c c|c} \hline ID & 0 & 1 & 2 & 3 & AVG \\ \hline AOS & 0.93 & 0.90 & 0.94 & 0.90 & 0.92 \\ CER & 0.0015 & 0.0033 & 0.0017 & 0.0064 & 0.0032 \\ TL & 0.99 & 0.99 & 0.99 & 0.98 & 0.99 \\ \hline \end{tabular}
\end{table} 
Table 4: Tracking results of our system on synthetic scene 3. IDs are assigned randomly and correspond to a single tracked worker each. Figure 7: Tracking results on computer generated hazardous situation on a heavy rainy day. Green rectangles denote ground truth labels whereas differently-colored rectangles illustrate the tracked pedestrian workers. to the training dataset of our YOLO detector. Second, we compared the performance of our tracking system on real world and corresponding 3D generated video sequences. We found that training on synthetic data samples significantly enhances the detection quality. In our experiments, we were able to boost our detector by 7.5 percentage points over a detector trained on a small real world dataset only. Though, the quality of the resulting detector is highly dependent on the choice of decent training samples. As for real world datasets, synthetic samples should also cover various environmental and lighting conditions. Furthermore, we found that a computer vision-based tracker performs very similarly on real world and 3D generated video sequences. Accordingly, the evaluation on synthetically generated scenes can already provide reliable insights regarding the strengths and weaknesses of a tracking system since its performance can be estimated considerably precisely. As a result, a vision-based tracking system can be tested on a variety of synthetically generated situations before being employed on a real construction site. By this, a flawless tracking can be guaranteed even for rare or exceptional situations. The findings of our experiments are in accordance with those from other application areas, but additionally highlight that synthetic data are capable of modeling even the complex and dynamic environments of construction sites realistically. For construction site-related applications, this validation is relevant to a special degree.