## 5 Conclusions
Since datasets are typically rare in this field, data augmentation using synthetic data could advance the use of vision-based approaches in the future. In particular, this facilitates incorporating conditionally occurring lighting and weather conditions into a dataset. By simulating, for example, snowfall or bright sunlight, in a virtual environment, data acquisition can be drastically accelerated and existing datasets can easily be completed. The use of synthetic data allows to model any weather and lighting conditions and to change the construction site setup at will. Otherwise, recordings all over the year and on different sites would be necessary to acquire a reasonable dataset. Further time savings and human resources result from the possibility of automatic labeling. Since the positions of all objects in a virtual scene are known exactly, labeling can be done fully automatically and with substantially higher precision than a manual labeling. Via the extension of various environmental conditions and a precise labeling, datasets can be prepared such that an optimal training is ensured. The resulting system can deal with manifold upcoming conditions without having seen these in the real world before. Besides the training of detectors, computer generated data is also valuable for the evaluation of vision systems. Again, virtual environments can be used to simulate a variety of scenarios to test the system on. This is particularly advantageous for events that rarely occur or are hazardous to replicate in the real world. Risky situations can be evaluated this way without exposing anyone to a serious hazard. In summary, our major conclusions are as follows: We have shown the applicability of synthetically generated data for vision systems in the area of construction sites. Furthermore, we highlighted the benefits of such data for training and evaluation purposes for the underlying machine learning algorithms. As this paper outlines an overview of the possibilities accompanied with synthetic data, future work should investigate either the training or the evaluation phase in more detail. For now, the limits of using synthetic data for both, training and evaluation remain unclear. It should also be determined to what extent the use of synthetic data is beneficial and if certain scenarios cannot be modeled sufficiently. The impact of such data on different machine learning algorithms could be another topic to focus on. Finally, a tracking system optimally trained and tested on a combination of real world and synthetic data should be employed on a real construction site for a large-scale case study. Conceptualization, M.N. and P.H. ; data curation, P.H. ; investigation, M.N. and P.H. ; methodology, M.N. ; project administration, M.N. ; resources, M.N. and P.H. ; software, M.N. ; supervision, M.K. ; validation, M.N. ; visualization, M.N. and P.H. ; writing--original draft, M.N. and P.H. ; writing--review and editing, M.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Acknowledgments:** The motion capture data used in this project was obtained from mocap.cs.cmu.edu. The CMU database was created with funding from NSF EIA-0196217. Furthermore, we acknowledge the support by the DFG Open Access Publication Funds of the Ruhr-Universitat Bochum. **Conflicts of Interest:** The authors declare no conflict of interest.