## 1 Introduction
Through the use of this dataset, [PERSON] and [PERSON] [25] improved a k-NN based parking space detection algorithm by six percentage points while simultaneously reducing the time for generating the dataset. As this paper focuses on the improvement of the detection and tracking of construction workers, a few particularities have to be taken into consideration. Construction sites depict more complex and harder to model environments than the well structured car park scenarios in [25]. Sites are essentially less structured and subject to greater changes. Construction material, machinery, and workers operate or interact with each other almost everywhere on the site. In the course of this, occlusions of the workers by construction machines and their loads occur frequently. Additionally, modeling human shapes and behaviors realistically is still a challenging task. Compared to the simple trajectories of cars, a worker's movement is significantly more complex and harder to predict. Dynamic environments such as these require a diversified dataset that covers all cases of working on a construction site, including dangerous situations which would be unethical to re-enact in a real world scenario. Lately, image style transfer learning such as Cycle-Consistent Generative Adversarial Networks (CycleGANs) [26] has been used for data augmentation. CycleGANs are able to learn a bidirectional image style mapping between two unlabeled image domains. The two image sets for training the GAN do not need to be paired sets, but there is still a significant amount of training data required. [PERSON] et al. [27] use this technique to enhance their synthetic dataset for crowd counting by augmenting the synthetic dataset with image styles from existing datasets. Since the synthetic data in [27] is collected from a video game, the image style is significantly different from actual camera footage. However, the small size and low variation in environmental conditions of common real world datasets of construction sites complicate the training of a GAN. Additionally, in this work, full control over the synthetic scenes' setup is required to sufficiently model potential hazardous situations, which includes the image style. Thus, this paper does not use CycleGANs for data augmentation. Hence, the aim of this paper is to investigate the usage of synthetically generated data for improving both, the training and the evaluation of construction site monitoring systems. For this, several 3D scenes of construction sites with varying lighting and environmental conditions are modeled. Sequences of these scenes are extracted to extend a self-recorded real world dataset. In order to identify the effects of synthetic data, a monitoring system is built that tracks pedestrian construction workers over time. The system detects workers in consecutive video frames using YOLOv3 [28] and tracks them by Kalman filtering. To prove the concept of this paper, first the detector's performance is evaluated when it is trained on the real world dataset only. Then, the results are compared to the performance when synthetic data is gradually added to the set of training samples. We found that providing well-chosen computer generated scenes to the training set improves the detection performance by 7.5 percentage points in mean average precision (mAP). In the end, the detector achieves a mAP of 94.0 %. If inaccuracies resulting from manual labeling and image scaling are taken into account, the mAP even increases to 99.2 %. In another setting, the tracking performance of the proposed monitoring system on real world and synthetic scenarios is compared. The experiment shows that the tracking system yields similar results on both kinds of datasets. This illustrates that synthetic data provides a reasonable alternative for the evaluation of the tracking performance on hazardous situations without exposing workers to risks. The remainder of this paper is organized as follows: Section 2 introduces the datasets as well as the tracking system.