This master thesis aims at creating a smart Pyroelectric Infrared based motion detector able to evaluate the room occupancy of an office. Since from the output signal of the motion detector it is not possible to directly evaluate how many people are present in the room, a counter of entrances and exits was used. In order to transform a motion detector into an entrance and exit detector, classical machine learning was exploited. Due to the limited time at disposal for the thesis, the PIR-based motion detector could only be mounted in one office and be analyzed during the spring season. For these reasons, it was not possible to record enough signal samples to generalize the results to another office or season.