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Panoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networks
dc.contributor.author | Thurnhofer-Hemsi, Karl | |
dc.contributor.author | López-Rubio, Ezequiel | |
dc.contributor.author | Domínguez, Enrique | |
dc.contributor.author | Luque-Baena, Rafael Marcos | |
dc.contributor.author | Molina-Cabello, Miguel Ángel | |
dc.date.accessioned | 2017-05-29T12:36:18Z | |
dc.date.available | 2017-05-29T12:36:18Z | |
dc.date.created | 2017 | |
dc.date.issued | 2017-05-29 | |
dc.identifier.uri | http://hdl.handle.net/10630/13761 | |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. | es_ES |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Teledetección | es_ES |
dc.subject.other | computer vision | es_ES |
dc.subject.other | PTZ cameras | es_ES |
dc.title | Panoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networks | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.centro | E.T.S.I. Informática | es_ES |
dc.relation.eventtitle | International Joint Conference on Neural Networks 2017 | es_ES |
dc.relation.eventplace | Anchorage, Alaska, Estados Unidos | es_ES |
dc.relation.eventdate | Mayo 2017 | es_ES |
dc.cclicense | by-nc-nd | es_ES |