## 2 Materials and Methods
### Dataset Acquisition  
Top-view scenes of construction sites have rarely been recorded. Accordingly, a dataset reasonable for our purpose has not been published yet. For that reason, we recorded video sequences to train and test our approach explicitly for the scope of this paper. It is important to know if different backgrounds and lighting conditions can have an influence on the tracking of construction workers. In addition, we want to test if our approach can distinguish between different moving objects. The videos were therefore taken in two scenarios with different levels of difficulty for our approach: in the first one, construction workers act on a uniformly plastered terrain, whereas a mixture of gravelled and plastered areas is chosen for the second scenario. While the first scene is illuminated well, the second scene is slightly overexposed. Both sequences are recorded by a non-pivoting camera at a height of 20 m, which is aligned to the ground and has a fixed position (see Figure 1). This results in a top-view perspective in the center of the image, but becomes oblique at the borders of the image. In accordance with a typical crane camera set up [28], all videos were recorded with a frame rate of 25 fps and a resolution of 1920 \(\times\) 1080 px. In both scenarios, construction workers wearing safety vests and helmets walk randomly through the camera's field of view including sudden stops and directional changes. They also interact with static construction specific elements such as pylons and barrier tapes. Of course, exposing workers intentionally to hazardous situations or heavy construction machinery is unwarrantable. Such hazardous situations are substituted by smaller moving vehicles instead which still allows for evaluating the correct classification of moving objects. In both video sequences, the construction workers are manually labeled by hand in order to prepare both the ground truth and the training data. Rectangular areas surrounding the worker's heads and shoulders are used to indicate the positions of the workers.  
Datasets for training, validation and evaluation are generated from the labeled sequences. For this, we divide each sequence into three shares of equal length. From the first share of both sequences, we generate the training dataset. This training dataset includes 1000 pedestrian construction worker samples (see Figure 2). Samples are only extracted from every 10 th frame to reduce the similarity among the samples. For generating the validation dataset, we proceed analogously with the second share of both sequences. The validation dataset also contains 1000 pedestrian construction worker samples. From the third part, we generate evaluation data that consist of video sequences with an average length of 12 s. From each sequence, we choose a representative scene which contains settings commonly occurring on construction sites. Both scenes show up to four pedestrian construction workers simultaneously and other objects colored similarly to the worker's safety vests that are either static or moving. The static objectsare red and white barrier posts, barrier tapes and a red barrier on a gravel area. These elements have similarities to the colors of the construction workers. Moving objects include a red vehicle that moves linearly in the same direction as a construction worker. In addition, the red color between the worn safety vest and the vehicle is similar. The workers walk through the scene while sometimes passing each other closely. In addition to these two scenes, we choose a third one to evaluate our approach with regard to moving vehicles. This scene shows a single worker walking while a car approaches him from behind.