Overhead-Based Image and Video Geo-Localization Framework
Abstract
This paper presents a geo-localization framework of street-level outdoor images using multiple sources of overhead reference imagery including LIDAR, Digital Elevation Maps and Multi-Spectral Land Cover/Use imagery. We describe five different matchers and an adaptive linear fusion process which combines individual matchers' probability maps into a single map. These matchers exploit mountain elevation profiles, rendered camera views, landmarks, landuse classes and building heights. We successfully validated our framework on 100 queries with geographic truth in two world regions (each of 10, 000km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) in the USA.
Cite
Text
Hammoud et al. "Overhead-Based Image and Video Geo-Localization Framework." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2013. doi:10.1109/CVPRW.2013.55Markdown
[Hammoud et al. "Overhead-Based Image and Video Geo-Localization Framework." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2013.](https://mlanthology.org/cvprw/2013/hammoud2013cvprw-overheadbased/) doi:10.1109/CVPRW.2013.55BibTeX
@inproceedings{hammoud2013cvprw-overheadbased,
title = {{Overhead-Based Image and Video Geo-Localization Framework}},
author = {Hammoud, Riad I. and Kuzdeba, Scott A. and Berard, Brian and Tom, Victor and Ivey, Richard and Bostwick, Renu and HandUber, Jason C. and Vinciguerra, Lori and Shnidman, Nathan and Smiley, Byron},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2013},
pages = {320-327},
doi = {10.1109/CVPRW.2013.55},
url = {https://mlanthology.org/cvprw/2013/hammoud2013cvprw-overheadbased/}
}