Occupancy information in indoor spaces is playing an increasingly important role in the development of smart applications. The need for this type of information covers a multitude of domains in the Smart Buildings paradigm such as improving energy saving or occupant comfort. For this reason, we can find many works in the literature focused on occupancy tracking/monitoring using solutions based on RGB cameras and computer vision techniques, sensors and machine learning techniques, or air quality control, among others. But these solutions have limitations. Some of them do not support the tracking of people between spaces, the time to update information is too long, or the system used is too intrusive. This paper presents a solution to estimate the occupancy level in indoor spaces of different areas through depth cameras. This approach also proposes the integration of neural networks to deal with situations where the data collected from the environment is incomplete, filling the gaps caused by occlusion or performance problems. Finally, an occupancy service has been designed and deployed in order to provide occupancy information to other applications, such as evacuation services. The experiments carried out show how it is possible to obtain an accuracy of 90.20% through this approximation. In addition, we face some of the limitations mentioned above: the solution allows tracking movement and occupancy in large spaces without (1) lighting dependencies and (2) the requirement for users to wear devices. This, and the high accuracy obtained make the proposed work a great alternative for occupancy estimation in indoor spaces.