Detecting and tracking the persons in a scene has been an active field of research in the area of computer vision for decades. One of the main applications are mobile platforms, such as autonomous cars or service robots. In this thesis, we are going to analyze different tracking approaches working on the image domain and have a deeper look at their errors, before we move forward to multi-object tracking from robots operating in the 3D world. We present our detection-tracking framework for systematic tracking evaluation and our insights on experiments in challenging environments. On top of that detection-tracking pipeline, we then run person analysis modules and look at the benefits of such an integration for an improved person-robot interaction. By utilizing the temporal information in the form of the person track identities, we enable a robust and efficient operation of expensive deep learning analysis methods on low-cost platforms. We conclude the thesis by presenting an explorative work on the integration of person re-identification and multi-object tracking to overcome common compromises of classical tracking approaches, taking a step towards an end-to-end, image-to-track paradigm.weiterlesen