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Aerial image sequence geolocalization with road traffic as invariant feature

Mattyus, Gellert and Fraundorfer, Friedrich (2016) Aerial image sequence geolocalization with road traffic as invariant feature. Image and Vision Computing, 52 (8), pp. 218-229. Elsevier. DOI: 10.1016/j.imavis.2016.05.014 ISSN 0262-8856

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Official URL: http://www.sciencedirect.com/science/article/pii/S0262885616301056

Abstract

The geolocalization of aerial images is important for extracting geospatial information (e.g. the position of buildings, streets, cars, etc.) and for creating maps. The standard is to use an expensive aerial imaging system equipped with an accurate GPS and IMU and/or do laborious Ground Control Point measurements. In this paper we present a novel method to recognize the geolocation of aerial images automatically without any GPS or (Inertial Measurement Unit) IMU. We extract road segments in the image sequence by detecting and tracking cars. We search in a database created from a road network map for the best matches between the road database and the extracted road segments. Geometric hashing is used to retrieve a shortlist of matches. The matches in the shortlist are ranked by a verification process. The highest scoring match gives the location and orientation of the images. We show in the experiments that our method can correctly geolocalize the aerial images in various scenes: e.g. urban, suburban, rural with motorway. Beside the current images only the road map is needed over the search area. We can search an area of 22500 km2 containing 32000 km of streets within minutes on a single cpu.

Item URL in elib:https://elib.dlr.de/104671/
Document Type:Article
Title:Aerial image sequence geolocalization with road traffic as invariant feature
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Mattyus, Gellertgellert.mattyus (at) dlr.deUNSPECIFIED
Fraundorfer, Friedrichfraundorfer (at) icg.tugraz.atUNSPECIFIED
Date:20 June 2016
Journal or Publication Title:Image and Vision Computing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:52
DOI :10.1016/j.imavis.2016.05.014
Page Range:pp. 218-229
Editors:
EditorsEmail
Frahm, Jan-Michaeljmf@cs.unc.edu
Pantic, Majam.pantic@imperial.ac.uk
Publisher:Elsevier
ISSN:0262-8856
Status:Published
Keywords:Image Processing, Computer Vision, Aerial images, Remote Sensing, Geolocalization, Georeferencing, Geotagging, Geometric Hashing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Mattyus, Gellert Sandor
Deposited On:20 Jun 2016 15:34
Last Modified:31 Jul 2019 20:01

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