Fusion of Heterogenous Sensor Data in Border Surveillance
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. I...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7351 |
| _version_ | 1797476982113435648 |
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| author | Luis Patino Michael Hubner Rachel King Martin Litzenberger Laure Roupioz Kacper Michon Łukasz Szklarski Julian Pegoraro Nikolai Stoianov James Ferryman |
| author_facet | Luis Patino Michael Hubner Rachel King Martin Litzenberger Laure Roupioz Kacper Michon Łukasz Szklarski Julian Pegoraro Nikolai Stoianov James Ferryman |
| author_sort | Luis Patino |
| collection | DOAJ |
| description | 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, 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%. |
| first_indexed | 2024-03-09T21:11:31Z |
| format | Article |
| id | doaj.art-d87fc93ed8664448b99244ea1fbc708c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:11:31Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d87fc93ed8664448b99244ea1fbc708c2023-11-23T21:47:48ZengMDPI AGSensors1424-82202022-09-012219735110.3390/s22197351Fusion of Heterogenous Sensor Data in Border SurveillanceLuis Patino0Michael Hubner1Rachel King2Martin Litzenberger3Laure Roupioz4Kacper Michon5Łukasz Szklarski6Julian Pegoraro7Nikolai Stoianov8James Ferryman9Department of Computer Science, University of Reading, Reading RG6 6DH, UKAIT Austrian Institute of Technology, 1210 Vienna, AustriaDepartment of Computer Science, University of Reading, Reading RG6 6DH, UKAIT Austrian Institute of Technology, 1210 Vienna, AustriaONERA, Département Optique et Techniques Associées (DOTA), Université de Toulouse, 31055 Toulouse, FranceITTI, 61-612 Poznan, PolandITTI, 61-612 Poznan, PolandAIT Austrian Institute of Technology, 1210 Vienna, AustriaBulgarian Defence Institute, 1592 Sofia, BulgariaDepartment of Computer Science, University of Reading, Reading RG6 6DH, UKWide 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%.https://www.mdpi.com/1424-8220/22/19/7351multi sensor fusionborder surveillanceobject detectionobject trackingthermal cameramovement sensors |
| spellingShingle | Luis Patino Michael Hubner Rachel King Martin Litzenberger Laure Roupioz Kacper Michon Łukasz Szklarski Julian Pegoraro Nikolai Stoianov James Ferryman Fusion of Heterogenous Sensor Data in Border Surveillance Sensors multi sensor fusion border surveillance object detection object tracking thermal camera movement sensors |
| title | Fusion of Heterogenous Sensor Data in Border Surveillance |
| title_full | Fusion of Heterogenous Sensor Data in Border Surveillance |
| title_fullStr | Fusion of Heterogenous Sensor Data in Border Surveillance |
| title_full_unstemmed | Fusion of Heterogenous Sensor Data in Border Surveillance |
| title_short | Fusion of Heterogenous Sensor Data in Border Surveillance |
| title_sort | fusion of heterogenous sensor data in border surveillance |
| topic | multi sensor fusion border surveillance object detection object tracking thermal camera movement sensors |
| url | https://www.mdpi.com/1424-8220/22/19/7351 |
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