## 2 Materials and Methods
### Hyperparameter Setting  
In the course of this paper, we developed a simple construction worker tracking system using deep learning. We did not aim at building the system to serve as an optimal ready-to-use safety device but to investigate the possibilities of synthetic data for training and evaluation of such approaches in construction site scenarios. Our system detects pedestrian construction workers in the camera images and tracks them over time in consecutive image frames. For detection, we apply YOLOv3 [28] as it combines classification and detection of the workers in a single CNN. The network was trained on parts of the labeled datasets described in Sections 2.1 and 2.2. Training the network from scratch is unfeasible due to the limited amount of real world data even though we have already made extensive use of YOLO's built-in data augmentation features. Hence, we rely on transfer learning to avoid overfitting the network to our small dataset during training. For this, we trained our network with a mini batch size of 64 based on the Darknet53 model, which has been pre-trained on ImageNet [29]. To quickly adjust the network towards the newly introduced class of pedestrian workers, we began the training with a high learning rate of 0.001. The learning rate was then scaled down by a factor of 0.1 each 3800 epochs until training  
Figure 3: Synthetic scenes 3–8. See Table 1 for descriptions. ended after 10,400 epochs. This facilitates a finer adjustment of the weights so that these converge towards an optimal result. For regularization, we adapted the weights by a momentum of 0.9 and a weight decay of 0.0005. Besides the hyperparameters directly affecting the learning behavior, YOLO's detection quality and especially its speed highly depend on the given input image size. Scaling down high resolution images may vanish smaller image features relevant for proper detection. Conversely, the larger the input image is, the longer it takes for YOLO to process the entire image. A suitable scaling of high resolution images with regard to the case of application has, thus, to be determined beforehand. By comparing the detector's performance on different input image sizes, we identify the size which yields optimal detection results in accordance with the preservation of real-time applicability. For this, we train and evaluate the detector's performance on various scales of the real world datasets described in Section 2.1. Since the input image size should be a multiple of 32 px because of YOLO's downsampling architecture, we start our tests with a size of 416 \(\times\) 416 px, which is equivalent to a factor of 13. For further tests, we gradually increase that factor each time by 3 up to an image size of 800 \(\times\) 800 px. The performances of these test runs are compared by the mAP score as proposed for the evaluation of the COCO Detection Challenge [30]. As depicted in Equation (1), besides averaging over recall values at \(i=[0,0.1,0.2,\ldots,1.0]\) with \(n_{i}\) values, this score also averages over different IoU thresholds \(j=[0.50,0.55,\ldots,0.95]\) in \(n_{j}\) steps. As a consequence, more accurate detection results are rewarded with a higher score. \[mAP=\frac{1}{n_{i}n_{j}}\sum_{i}\sum_{j}\text{Precision}(\text{Recall}_{i,j}) \tag{1}\]  
For reasonable monitoring or assistance systems, an accurate localization of pedestrian workers on construction sites is desirable. Thus, we choose the COCO mAP score as this score implicitly indicates the localization accuracy. Although we found that detection accuracy generally rises with increasing image size, the detection speed rigorously drops. At the base image size, we measure a detection time for a single image of 46 ms with a mAP of 76.9 % on the evaluation dataset. At our maximum image size, the detection time increased to 97 ms. This corresponds to about 10 frames per second (fps), which would inhibit a reliable tracking as addressed in Section 2.4.