## 6 Conclusions  
In this study, we investigated whether deep learning methods surpass classical approaches on construction worker monitoring despite their limitations. We chose YOLOv3 for the CNN and a classical approach based on a soft cascaded classifier as representatives for our comparison. The trained detectors were then embedded in a tracking system to track construction workers in video sequences. To evaluate the tracking systems under various conditions, we generated different video sequences. These contain different environmental and lighting conditions as well as stationary and moving non-worker objects. Both tracking systems were applied to the same sequences to ensure a proper comparison.  
As our experiments showed, the classical approach clearly outperforms the CNN on the detection task in terms of quality and speed. The lack of quality is most likely due to an insufficient amount of training data and the heavy downscaling of the images. The low detection speed of substantially less than 22 fps is affiliated to the high computational costs. However, the tracking experiment showed that the CNN's drawbacks are fully compensated even by a simple tracking method. Both approaches showed satisfying results when tracking workers under ideal conditions. They were even able to suppress a false tracking of any stationary or moving non-worker object. Nevertheless, both tracking systems reveal deficiencies when applied to overexposed conditions. While the CNN keeps precise track of the worker's dimensions, localization becomes inaccurate. The classical approach behaves exactly vice versa. According to these findings, there is no optimal monitoring approach in general. A suitable approach has to be chosen with respect to the demands of the particular application.  
With our example scenarios, we showed that with both approaches a satisfactory tracking of the construction workers from the top view is possible. Nevertheless, it would be an advantage to know whether satisfactory tracking is also achieved with more complex work processes with different moving machines, different construction site constellations and different weather conditions. Therefore, future work should compare the presented tracking approaches with scenes that have a different focus. In this case, optical flow techniques can additionally be used to enable tracking also with cameras mounted, e.g., on the crane boom. The background subtraction would consider known crane movements and thus dynamically changing backgrounds would be taken into account. Furthermore, future work should concentrate on substantially increasing the size of the datasets. By this, the training of CNN-based approaches can be improved. This helps the detector to develop a better generalization ability, which may advance the tracking results especially under difficult conditions. Reasonable strategies for the extension may be alternative data augmentation techniques as well as computer generated data.  
Conceptualization, M.N.; data curation, M.N.; investigation, M.N.; methodology, M.N.; project administration, M.N.; software, M.N. and D.P.; supervision, M.K.; validation, D.P.; visualization, D.P.; writing--original draft, M.N.; and 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.  
The authors declare no conflict of interest.