In the research field of human–robot-interaction, detection of people is of central importance. Moving robotic platforms create additional challenges due to an absence of a defined area of interaction between humans and robots. The use of a mobile robotic platform to provide personalized assistance requires that tracking and identification of the interaction partner in a group of people can be ensured at all times. This work contributes to an application that can detect and distinguish multiple people in public spaces. We use Mask R-CNN followed by DeepSORT for the differentiation and tracking of each individual in a video by assigning a unique ID. We apply our approach to the Multi-Object Tracking and Segmentation (MOTS20) data set and show that our method successfully performs person detection, differentiation, and tracking at 5 frames per second. Using the three evaluation metrics Multiple Object Tracking (MOTA), the Higher Object Tracking (HOTA) and the Identification metric (IDF1), we achieved an accuracy of 49.69%, 48.35% and 56.14%, respectively. Our results show good accuracy, but also possibilities for future improvements. We show how reflections, shadows, human-like clothing, and poor annotations influence the precision negatively.