## 2 Materials and Methods
### Real World Dataset  
Since video datasets of construction sites including pedestrian workers from a top view perspective are rarely publicly available, we assembled a small generic dataset on our own. For this, we recorded several sequences of walking construction workers using a camera mounted in a height of around 20 m. This results in a bird's-eye-like view in the center of the images whereas the view becomes oblique near the borders. A reasonable dataset should exhibit a large variation in the data samples. This includes different lighting and environmental conditions of the scenes as well as various objects and differences in their appearance. To realize variation in our dataset, we chose two environmental scenarios. These contain different ground textures and lighting conditions as well as different types of construction sites and present objects. While the ground in one scene is uniformly paved (see Figure 1a), the other scene exhibits sandy, paved, and graveled parts, as shown in Figure 1b. The scenes are recorded at different daytimes to include different but realistic lighting conditions. This results in a well-exposed to overexposed illumination in the one scene at noon whereas in the other scene, in the evening, long shadows and lateral illumination are present. The scenes also vary in their setup and item selection. One shows a roadwork-like setup containing parked and driving cars as well as striped delineators, poles, and garbage cans. The other is a building work-like scene including a construction barrier and barrier tape. In both scenes, there are areas of different illumination conditions ranging from slightly underexposed to a high overexposure. The pedestrian workers wear safety vests and helmets throughout and walk randomly across both scenes including sudden stops and directional changes. They also interact with and relocate static objects like pylons and barriers.  
The resulting dataset is labeled manually to provide ground truth for training and evaluation purposes. For this, workers are identified by rectangular bounding boxes closely framing their heads, shoulders, and safety vests. In Figure 1, these labels are represented by green rectangles.  
The dataset is split into two separate subsets. A share of the recorded video sequences is assigned to each subset. This way, the construction worker detector can be trained on one subset while the evaluation is carried out on the other subset unknown to the detector until then. The shares are assigned in a way that both environmental scenarios are equally distributed within each. Each subset only obtains every tenth frame of the assigned sequences in order to ensure a sufficient amount of variation among the images. The number of workers visible in each frame varies as workers occasionally entered and left the camera's field of view during recording. Although we do not identify particular workers, the number of visible workers per image and their locations are known since the sequences were manually labeled beforehand. In the following, each visible worker per image is referred to as a worker instance. In conclusion, the subsets are organized as follows: the training set consists of 660 images of both environmental conditions containing 1000 pedestrian worker instances while the evaluation set shows 2750 worker instances in 1300 images of different sections of the recorded sequences.