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Fusion of heterogenous sensor data in border surveillance

Patino, L., Hubner, M., King, R., Litzenberger, M., Roupioz, L., Michon, K., Szklarski, L., Pegoraro, J., Stoianov, N. and Ferryman, J. (2022) Fusion of heterogenous sensor data in border surveillance. Sensors, 22 (19). 7351. ISSN 1424-8220

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To link to this item DOI: 10.3390/s22197351


Wide 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, in this paper 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%.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:107509


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