User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models

Eric Tzeng, Andrew Zhai, Matthew Clements, Raphael Townshend, Avideh Zakhor; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 237-244

Abstract


We propose a system for user-aided visual localization of desert imagery without the use of any metadata such as GPS readings, camera focal length, or field-of-view. The system makes use only of publicly available digital elevation models (DEMs) to rapidly and accurately locate photographs in non-urban environments such as deserts. Our system generates synthetic skyline views from a DEM and extracts stable concavity-based features from these skylines to form a database. To localize queries, a user manually traces the skyline on an input photograph. The skyline is automatically refined based on this estimate, and the same concavity-based features are extracted. We then apply a variety of geometrically constrained matching techniques to efficiently and accurately match the query skyline to a database skyline, thereby localizing the query image. We evaluate our system using a test set of 44 ground-truthed images over a ystemuukm garegion of interest in a desert and show that in many cases, queries can be localized with precision as fine as 100 m es.

Related Material


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[bibtex]
@InProceedings{Tzeng_2013_CVPR_Workshops,
author = {Tzeng, Eric and Zhai, Andrew and Clements, Matthew and Townshend, Raphael and Zakhor, Avideh},
title = {User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2013}
}