The goal of this thesis is to gain insights from a given dataset into the foraging behavior of a beehive. The dataset is a collection of video recordings of bees exiting and entering a hive through a transparent pipe. Each bee is marked with an individual ID-tag. To this end, first an already existing ID-tag localizer, which was originally trained on data with different illumination and background, is adapted to improve its detection performance on the new dataset. Next, the fine-tuned localizer is used in combination with the Beesbook tracking pipeline to extract exit and entry tracks for each individual bee from the raw video data. The results are presented, using descriptive statistics, and visualized through several plots. Finally, an algorithm to cluster bees into peer groups, based on the tracking data, is suggested.