Humans interact more and more at a distance these days. Where currently, this interaction is mainly visual and auditory, Tactile Internet (TI) also allows for remote kinesthetic interactions. With the devel- opment of TI, a paradigm shift is on the horizon where the current internet evolves from content delivery to a skill-set delivery network. Imagine the possibilities when specialists in whatever field can, in real- time, apply their skills everywhere in the world from within their living room. To make this a reality, TI applications have to be able to provide feedback to the user with ULL. The speed of light, however, constrains the extent to which TI can be used at large distances. This thesis hence takes a closer look at the problems that arise when long-distance TI is to be used and tries to tackle them. These problems are tackled by building a system based on Model-Mediated Teleoperation (MMT), where lo- cal simulations of the environments are used. A local feedback loop is instantiated through these local simulations, which lowers the system’s dependence on the network performance. The former control loop, including the network, now becomes a corrective loop to prevent mismatches. This thesis focuses on the part where the local simulation is corrected by the real-life data received over the network. The question is how real-time dynamic environments can be captured and kept track of for use in MMT. We identify accurate tracking of objects in the environment as the critical building block enabling MMT with dynamic objects. Consequently, the first 6 Degrees of Freedom (DoF) tracking application designed for use in TI applications is created and evaluated. Through this research, the first steps are set towards a full-blown MMT system that can capture the remote environment successfully.