Evidence map›Paper›PMID 36236450›Full record

ArticleSensors (Basel, Switzerland)2022

Fusion of Heterogenous Sensor Data in Border Surveillance.

Luis Patino, Michael Hubner, Rachel King, Martin Litzenberger, Laure Roupioz, Kacper Michon, Łukasz Szklarski, Julian Pegoraro, Nikolai Stoianov, James Ferryman

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 5 countries.

Luis PatinoDepartment of Computer Science, University of Reading, Reading RG6 6DH, UK.ORCID 0000-0002-6716-0629
Michael HubnerAIT Austrian Institute of Technology, 1210 Vienna, Austria.ORCID 0000-0002-1640-5614
Rachel KingDepartment of Computer Science, University of Reading, Reading RG6 6DH, UK.
Martin LitzenbergerAIT Austrian Institute of Technology, 1210 Vienna, Austria.
Laure RoupiozONERA, Département Optique et Techniques Associées (DOTA), Université de Toulouse, 31055 Toulouse, France.ORCID 0000-0002-9988-3071
Kacper MichonITTI, 61-612 Poznan, Poland.
Łukasz SzklarskiITTI, 61-612 Poznan, Poland.
Julian PegoraroAIT Austrian Institute of Technology, 1210 Vienna, Austria.
Nikolai StoianovBulgarian Defence Institute, 1592 Sofia, Bulgaria.
James FerrymanDepartment of Computer Science, University of Reading, Reading RG6 6DH, UK.
Austrian Institute of Technology · ATUniversity of Reading · GBInstytut Technik Telekomunikacyjnych i Informatycznych (Poland) · PL"Professor Tsvetan Lazarov" Defence Institute · BGUniversité Fédérale de Toulouse Midi-Pyrénées · FR

Funding

European Commission 787021
6 · The paper itself

Abstract

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%.

Indexed as

RadarHumansMonitoring, Physiologicborder surveillancemovement sensorsmulti sensor fusionobject detectionobject trackingthermal camera

Identifiers

PMID36236450
PMCPMC9571058
OpenAlexW4297423557

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.