RT Conference Proceedings
T1 Panoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networks
A1 Thurnhofer-Hemsi, Karl
A1 López-Rubio, Ezequiel
A1 Domínguez-Merino, Enrique
A1 Luque-Baena, Rafael Marcos
A1 Molina-Cabello, Miguel Ángel
K1 Teledetección
AB The construction of a model of the background of ascene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed method is effective and suitable to use for real-time video surveillance applications.
YR 2017
FD 2017-05-29
LK http://hdl.handle.net/10630/13761
UL http://hdl.handle.net/10630/13761
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026