A multi-feature object association framework for overlapped field of view multi-camera video surveillance systems

This work describes a data fusion technique to improve performances in objects localization and tracking for automatic video surveillance systems. The developed strategy is designed to perform well in case of interaction among objects, i.e. when the moving objects to track, and whose position we want to locate on the common map reference system, result superimposed in the image plane. In order to solve such complex situations, different kind of techniques have been integrated but the focus of the paper is on the data association step in the fusion chain. As discussed in the text, failing in the association phase means computing wrong position during fusion process. The performances of the developed technique has been evaluated on sequences of real images and experimental results show the validity of the approach in the reduction of association errors during occlusion phases.