Context data to improve association in visual tracking systems

Abstract

A key aspect in visual surveillance systems is robust movement segmentation, which is still a difficult and unresolved problem. In this paper, we propose an architecture based on a two-layer image-processing modules: General Tracking Layer (GTL) and Context Layer (CL). GTL describe a generic multipurpose tracking process for video-surveillance systems. CL is designed as a symbolic reasoning system that manages the symbolic interface data between GTL modules in order to asses a specific situation and take the appropriate decision about visual data association. Our architecture has been used to improve the association process of a tracking system and tested in two different scenarios to show the advantages in improved performance and output continuity. © Springer-Verlag Berlin Heidelberg 2007.