3D measures exploitation for a monocular semi-supervised fall detection system3D measures exploitation for a monocular semi-supervised fall detection system Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-042015enFalls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. Thus, the development of robust home surveillance systems is of great importance. In this article, such a system is presented, which tries to address the fall detection problem through visual cues. The proposed methodology utilizes a fast, real-time background subtraction algorithm, based on motion information in the scene and pixels intensity, capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object. At the same time, it exploits 3D space’s measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning approach. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.http://creativecommons.org/licenses/by/4.0/Multimedia Tools and Applications1-33 Matsatsinis Nikolaos Ματσατσινης Νικολαος Doulamis, Anastasios Eftychios Protopapadakis Konstantinos Makantasis Nikolaos Doulamis Springer Verlag Image motion compensation Motion compensation (Optics) Stabilization, Image Vibration reduction (Optics) image stabilization image motion compensation motion compensation optics stabilization image vibration reduction optics Semi-supervised learning