Context Data to Improve Association in Visual Tracking Systems

Authors:
A. M. Sánchez;M. A. Patricio;J. García;J. M. Molina
Affiliations:
Universidad Carlos III de Madrid, Computer Science Department, Applied Artificial Intelligence Group, Avda. Universidad Carlos III 22, 28270 Colmenarejo (Madrid),;Universidad Carlos III de Madrid, Computer Science Department, Applied Artificial Intelligence Group, Avda. Universidad Carlos III 22, 28270 Colmenarejo (Madrid),;Universidad Carlos III de Madrid, Computer Science Department, Applied Artificial Intelligence Group, Avda. Universidad Carlos III 22, 28270 Colmenarejo (Madrid),;Universidad Carlos III de Madrid, Computer Science Department, Applied Artificial Intelligence Group, Avda. Universidad Carlos III 22, 28270 Colmenarejo (Madrid),
Venue:
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Year:
2007

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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.