## 1 Introduction
Employing a setup that utilizes radio-frequency identification (RFID) technology enables the warning of workers and machine operators whenever a tagged worker gets into the range of a machine's reader [13] or specific zones [14]. Other approaches develop systems noticing pedestrian workers when entering certain zones by localizing them using ultra-wideband (UWB) tags [7]. Although the accuracy can be improved by combining RFID technology with ultrasound [15], a precise localization and tracking of workers in real time constitutes a challenging task [7] which has not been sufficiently solved in general yet [16]. Besides technical deficiencies, the deployment of such a system for the implementation of a tracking application on a real construction site requires a tag for each worker. Additionally, at least three receivers per monitored area have to be installed in the case of two-dimensional tracking which results in high acquisition costs [6]. Furthermore, workers perceive the attachment of tags with unique identifiers (IDs) to their gear to be obtrusive and to cause discomfort [17]. In contrast to tag-based methods, camera-based tracking approaches provide cost-effective surveillance alternatives. The costs amount to one camera per monitored area and there are no further costs for additional equipment for the workers. In research, some effort has already been made to detect construction worker's in camera images [18]. [PERSON] and [PERSON] [8] built a real-time capable detection scheme to recognize construction workers in camera images for initializing a visual tracker. They used background subtraction via a median filter to find moving objects within the images. Then, a pedestrian detection is performed on these objects using a support vector machine (SVM) which operates on Histogram of Oriented Gradients (HOG) features. Exploiting the loud colors of the worker's safety vests, construction workers are identified from the pedestrian detections by clustering hue, saturation, value (HSV) color histograms using a k-Nearest Neighbors (k-NN) algorithm. [PERSON] and [PERSON] [19] also decided on background subtraction to identify moving objects before classifying these by a neural network approach. In [20], the background subtraction is exchanged by a sliding window approach. However, similar to [PERSON] and [PERSON] [8], they used HOG and color features but concatenate them to a single vector which is passed to a SVM in order to identify workers. Using color and spatial models classified by a Gaussian kernel, [PERSON] et al. [21] decided on a similar strategy. In recent years, deep neural network based approaches have become prominent. To recognize worker's activities, [PERSON] et al. [22] applied temporal segment networks. [PERSON] et al. [23] used an region based convolutional neural network (R-CNN) based on ResNet to detect workers under changing conditions. [PERSON] et al. [24] proposed an approach which detects construction workers in oblique images of cameras mounted in heights. They applied YOLO for detection but only achieved a precision of about 75%. [PERSON] et al. [25] proposed a convolutional neural network (CNN)-based concept detecting workers in top-view images. To cope with high altitudes, their approach relies on several zoom levels each with a separate CNN for detection. An additional meta CNN is used to choose the correct zoom level for a certain height. Despite the use of transfer learning techniques, CNN-based approaches usually require large amounts of training data. Corresponding data, which represent construction sites from a top view perspective, are rarely available. In addition, the generation of a sufficiently large dataset is a time-consuming and demanding task. Beyond that, these networks commonly operate on small image sizes of about \(400\times 400\,\mathrm{px}\). High resolution images cannot be processed in real time without disproportionately large amounts of computational power or without drastically downscaling images.