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.
Funded by CICYT TEC2005-07186, CAM 15 MADRINET S- 0505/TIC/0255 and IMSERSO AUTOPIA.
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Sánchez, A.M., Patricio, M.A., García, J., Molina, J.M. (2007). Context Data to Improve Association in Visual Tracking Systems. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_23
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DOI: https://doi.org/10.1007/978-3-540-73055-2_23
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