Fusion of Heterogenous Sensor Data in Border Surveillance

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dc.contributor.authorPatino, Luis
dc.contributor.authorHubner, Michael
dc.contributor.authorKing, Rachel
dc.contributor.authorLitzenberger, Martin
dc.contributor.authorRoupioz, Laure
dc.contributor.authorMichoń, Kacper
dc.contributor.authorSzklarski, Łukasz
dc.contributor.authorPegoraro, Julian
dc.contributor.authorStoianov, Nikolai
dc.contributor.authorFerryman, James
dc.contributor.organizationUniversity of Readingen
dc.contributor.organizationAIT Austrian Institute of Technologyen
dc.contributor.organizationONERA, Département Optique et Techniques Associées (DOTA), Université de Toulouseen
dc.contributor.organizationITTIen
dc.contributor.organizationBulgarian Defence Instituteen
dc.date.accessioned2024-01-19T11:42:49Z
dc.date.available2024-01-19T11:42:49Z
dc.date.issued2022
dc.description.abstractWide area surveillance has become of critical importance, particularly for border control between countries where vast forested land border areas are to be monitored. In this paper, we address the problem of the automatic detection of activity in forbidden areas, namely forested land border areas. In order to avoid false detections, often triggered in dense vegetation with single sensors such as radar, we present a multi sensor fusion and tracking system using passive infrared detectors in combination with automatic person detection from thermal and visual video camera images. The approach combines weighted maps with a rule engine that associates data from multiple weighted maps. The proposed approach is tested on real data collected by the EU FOLDOUT project in a location representative of a range of forested EU borders. The results show that the proposed approach can eliminate single sensor false detections and enhance accuracy by up to 50%.en
dc.description.sponsorshipKomisja Europejska
dc.identifier.citationPatino, L.; Hubner, M.; King, R.; Litzenberger, M.; Roupioz, L.; Michon, K.; Szklarski, Ł.; Pegoraro, J.; Stoianov, N.; Ferryman, J. Fusion of Heterogenous Sensor Data in Border Surveillance. Sensors 2022, 22, 7351. https://doi.org/10.3390/s22197351.en
dc.identifier.doi10.3390/s22197351
dc.identifier.issn1424-8220
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/23668
dc.language.isoen
dc.publisherMDPIen
dc.rightsUznanie autorstwa 4.0 Międzynarodowe*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmulti sensor fusionen
dc.subjectborder surveillanceen
dc.subjectobject trackingen
dc.subjectthermal cameraen
dc.subjectmovement sensorsen
dc.subjectobject detentionen
dc.titleFusion of Heterogenous Sensor Data in Border Surveillanceen
dc.typearticleen
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