2026-05-31T16:48:34Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/137612026-02-03T12:10:02Zcom_10630_2254col_10630_37959

Panoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networks Thurnhofer-Hemsi, Karl López-Rubio, Ezequiel Domínguez-Merino, Enrique Luque-Baena, Rafael Marcos Molina-Cabello, Miguel Ángel Teledetección The construction of a model of the background of a scene 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. 2017-05-29T12:36:18Z 2017-05-29T12:36:18Z 2017 2017-05-29 conference output http://hdl.handle.net/10630/13761 eng International Joint Conference on Neural Networks 2017 Anchorage, Alaska, Estados Unidos Mayo 2017 open access by-nc-nd