The proposed system is aimed to detect and classify 3D moving objects for security control of unmanned automatic railway stations. Most common approaches are based on active sensors like optical barriers or laser scanning devices. The proposed approach, named 3D MODS, is based on stereo vision technology, using a prediction-verification paradigm. Adaptive change detection is performed at video rate to immediately detect moving objects in the scene. Object features are collected by "scanning" the scene with different parallel planes at variable height, to verify the volumetric consistency of the detected object.

A prediction of stereo correspondence is performed, using homographic transformation on the set of predefined 3D planes, to verify whether the detected change is really a moving 3D object with a significant size, or it is just a phantom caused by shadows or highlights. A simple classification scheme is currently implemented, to decide for an alarm candidate in case of relevant object size, but much more complex and flexible solutions are possible, to recognize all the relevant objects in the scene and achieve much more robust alarm detection performance.