Localization and classification of partially overlapped objects using self-organizing trees

This paper exploits an innovative technique to improve performances related to localization, tracking and classification of objects in a video surveillance system. The developed strategy has been applied to the problem of interaction between objects, i.e. well tuned traditional algorithms can able to track and classify objects whenever they enter the scene well-isolated from the other moving objects, but state-of-the-art techniques fail when an occlusion situation is verified from the beginning. The performances of the developed algorithms have been evaluated on sequences of real images and experimental results have shown the validity of the approach.