## 2 Materials and Methods
### Classical Detection Approach  
Based on our theoretical concept previously proposed in [27], we implemented an explicit approach to detection of construction workers in top view images. As shown in Figure 3, detection is done by first extracting relevant regions of interest (RoIs) from the current camera image. Afterwards, each region is classified to determine whether it contains a worker or not. Figure 1: The construction workers are captured by a stationary camera which is located 20 m above the ground. The camera is pointed at the ground. Figure 2: The training dataset contains construction workers taken from the top view. The workers are in different orientations and illuminated in different ways. Most previously implemented image-based approaches in civil engineering are based on a frontal camera perspective. In our case, however, the camera is mounted at a height in order to monitor a large area and to satisfy the requirements of different applications. This leads to a top-view perspective. Most features that make up a pedestrian, such as legs and arms, are usually covered by his own body due to the perspective. Although there are already approaches for the general detection of pedestrians in top-view images, detecting pedestrian workers can be easy by leveraging typical features that characterize those workers. On construction sites, workers wear helmets and safety vests with loud colors that constitute clearly visible and prominent features. Similar to the findings of [PERSON] and [PERSON] [8], a combination of motion, color and shape features is more reasonable for a proper detection in our scenario than the use of common pedestrian detection features. While motion is a fundamental characteristic of pedestrians, it is not unique to them. Other objects such as construction vehicles may also move through the cameras field of view so that classification by motion is unrewarding. Notwithstanding, constraining the detection to image regions containing movement spares the expense of investigating the entire image. This improves the subsequent detection in two different ways. On the one hand, applying the classifier to only a few regions of the image significantly speeds up the detection process which allows for the monitoring of substantially larger areas of a site without an appreciable time lag. On the other hand, this preselection excludes most image regions not containing any construction workers from the further detection process which highly reduces false positive detections in advance. To find regions of motion, moving foreground objects have to be separated from the background. For this, Gaussian mixture models are frequently used and well established methods estimating a scene's background are available [8]. We use an improved adaptive Gaussian mixture model (GMM) approach [29], because it is insensitive to background movements that often occur in outdoor scenarios, e.g., wobbling bushes or leaves blown by the wind [30]. To adapt to changes in the scene such as varying illumination conditions or newly positioned and removed static objects, the mixtures of Gaussians are learned and adjusted over time. Accordingly, the Gaussians' parameters, their number per pixel, as well as the learning rate are updated online by the adaptive approach while applying the model to consecutive video frames. Comparing the current frame (see Figure 4a) to the learned background model results in a binary segmentation image which indicates fore- and background pixels. In the next step, all connected foreground pixels are aggregated to regions of motion by blob detection. For this purpose, an algorithm is used that  
Figure 3: Concept of the classical detection approach. Each time step \(t\) corresponds to an image frame. The current frame in time step \(t_{i}\) is subtracted from a background model which is generated from images of the previous time steps. This results in foreground blobs which represent RoIs.