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Your search keyword '"Frahm, Jan"' showing total 17 results
Start Over Author "Frahm, Jan" Publisher elsevier bv
17 results on '"Frahm, Jan"'

17. Aerial image sequence geolocalization with road traffic as invariant feature

Author

Friedrich Fraundorfer, Gellert Mattyus, Frahm, Jan-Michael, and Pantic, Maja

Subjects
Computer science, Image Processing, Computer Vision, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, 02 engineering and technology, Remote Sensing, Line segment, Inertial measurement unit, Geolocalization, Georeferencing, Aerial images, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Road map, Invariant (mathematics), Geotagging, Aerial image, 021101 geological & geomatics engineering, Photogrammetrie und Bildanalyse, business.industry, Geolocation, Geometric Hashing, Signal Processing, Global Positioning System, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, Geometric hashing, business
Abstract

The geolocalization of aerial images is important for extracting geospatial information (e.g. the position of buildings, streets, and cars) 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 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, and rural with motorway. Besides the current images only the road map is needed over the search area. We can search an area of 22,500km2 containing 32,000km of streets within minutes on a single cpu. We utilize roads as appearance invariant features for geolocalization.Using the road map an image database over the search area is not needed.We present a geometric hashing method to match partial line segments to line structures.The search method does not need a complete road detector which detects relations between roads as a graph, e.g. intersections.Using car tracks as road segment detection is invariant to different light conditions, seasonal changes and scale.

Published
2016

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