## 5 Discussion  
As shown by our experiment regarding the detection quality, both methods outperform the approach of [PERSON] et al. (2019) by far. While [PERSON] et al. achieved a precision of 75.1%, our approaches reach 99.8% and 99.2%, respectively. A proper comparison of the results is delicate since the datasets used for training and evaluation differ from each other. However, tests using YOLO with the same input image size as proposed by [PERSON] et al. yield similar results, which indicates at least a weak comparability of the setups. This confirms that the input image size is a crucial parameter for a proper detection when using YOLO, as the objects to be detected shrink proportionally to the size of the images. Nevertheless, this experiment also shows that the results of the classical method exceed those of YOLO by about 5%. As mentioned above, the image size may be a reason for this. The classical approach operates on the original high resolution, exhibiting more detailed features than the drastically downscaled images used by YOLO. A second reason may arise from the limited dataset since it is known that CNNs require a vast amount of training data. The number of provided training samples may have been too small to sufficiently adapt to the class of workers,although we deliberately used a pre-trained network which already developed general feature descriptors for classification. On the contrary, the findings in Section 3.1 highlight that classical computer vision approaches cope significantly better with fewer samples than CNNs. Both reasons emphasize common practical issues concerning data gathered in the context of construction site monitoring. Since computer vision approaches are rarely applied in the field, dataset generation has rarely been regarded a subject until now. Beyond this, generating a reasonable dataset covering all environmental and lighting conditions is extremely time-consuming and tedious. Given such a dataset, the small size of the pedestrian workers within the images remains to be a limiting factor. Thus, choosing a classical detection approach over a CNN is desirable to obtain the best detection results for the purpose of monitoring pedestrian workers. However, our tracking experiments showed that both approaches yield suitable results. As can be seen from the results of Scene 1, the evaluated systems perform similarly well on a well-illuminated environment. Both approaches exhibit an excellent accuracy with very low CER and high AOS of about 90%. Furthermore, the high TL rates indicate their robustness. The slight deviations from the optimum are mainly due to workers entering and leaving the scene. In these situations, workers are located at the image boarders and may be only partly visible. This complicates the detection of the workers so that tracks may be set up late or may terminate early. Apart from these effects, the tracking performed by both approaches is very precise despite the CNN's limitations in detection quality and speed. This shows that even simple tracking methods can compensate for lower detection rates. However, Scene 1 also shows that both approaches have issues with high overexposure. In both cases, the AOS of the regarding track drops noticeably accompanied by a raise in CER. The worker is still tracked sufficiently by both systems but the accuracy dramatically suffers from the overexposure. The CNN-based approach seems to cope with this issue slightly better than the classical system. This is no reliable statement yet as there is only a single instance of evidence available in the data. The results of Scene 2 provide further insights into the subject of overexposure. On this scene, the TL rates remain almost stable indicating a satisfying robustness. This ensures reliable tracking with both tracking systems despite a certain degree of overexposure. However, the CNN-based approach achieves significantly better AOS rates in this slightly overexposed environment. The CER rates fluctuate more than with the classical approach.