Accurate monitoring of space occupancy has become a central challenge for smart buildings. Real-time knowledge of how many people are inside a room enables energy savings, supports safety, and prevents overcrowding in places such as offices, museums, and public events. At the same time, privacy requirements make camera-based systems unsuitable, since they identify individuals rather than providing only anonymous counts. This thesis introduces an embedded people counting system based on an overhead Time-of-Flight (ToF) sensor, designed to operate in real time on resource-constrained devices. The core contribution is the design of a complete pipeline that brings together several original elements: the construction of a dedicated dataset with 3D annotations, the development of a lightweight neural network called ToFNet-light that predicts both the number of people and their centroids, and a tailored tracking logic for single and multiple crossings.