Where am I looking at? Joint location and orientation estimation by cross-view matching

Shi, Y; Yu, X; Campbell, D; Li, H

Where am I looking at? Joint location and orientation estimation by cross-view matching

Shi, Y Yu, X

Campbell, D Li, H

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, 00, pp. 4063-4071
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Shi, Y
dc.contributor.author	Yu, X https://orcid.org/0000-0002-0269-5649
dc.contributor.author	Campbell, D
dc.contributor.author	Li, H
dc.date	2020-06-13
dc.date.accessioned	2021-04-16T00:54:11Z
dc.date.available	2021-04-16T00:54:11Z
dc.date.issued	2020-01-01
dc.identifier.citation	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, 00, pp. 4063-4071
dc.identifier.isbn	978-1-7281-7168-5
dc.identifier.issn	1063-6919
dc.identifier.issn	2575-7075
dc.identifier.uri	http://hdl.handle.net/10453/148151
dc.description.abstract	Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (e.g., satellite) images. Existing approaches treat the task as a pure location estimation problem by learning discriminative feature descriptors, but neglect orientation alignment. It is well-recognized that knowing the orientation between ground and aerial images can significantly reduce matching ambiguity between these two views, especially when the ground-level images have a limited Field of View (FoV) instead of a full field-of-view panorama. Therefore, we design a Dynamic Similarity Matching network to estimate cross-view orientation alignment during localization. In particular, we address the cross-view domain gap by applying a polar transform to the aerial images to approximately align the images up to an unknown azimuth angle. Then, a two-stream convolutional network is used to learn deep features from the ground and polar-transformed aerial images. Finally, we obtain the orientation by computing the correlation between cross-view features, which also provides a more accurate measure of feature similarity, improving location recall. Experiments on standard datasets demonstrate that our method significantly improves state-of-the-art performance. Remarkably, we improve the top-1 location recall rate on the CVUSA dataset by a factor of 1.5× for panoramas with known orientation, by a factor of 3.3× for panoramas with unknown orientation, and by a factor of 6× for 180 -FoV images with unknown orientation. ◦
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
dc.relation.ispartof	IEEE/CVF Conference on Computer Vision and Pattern Recognition
dc.relation.ispartofseries	IEEE Conference on Computer Vision and Pattern Recognition
dc.relation.isbasedon	10.1109/CVPR42600.2020.00412
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Where am I looking at? Joint location and orientation estimation by cross-view matching
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Seattle, WA, USA
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-16T00:54:10Z
pubs.finish-date	2020-06-19
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-06-13
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (e.g., satellite) images. Existing approaches treat the task as a pure location estimation problem by learning discriminative feature descriptors, but neglect orientation alignment. It is well-recognized that knowing the orientation between ground and aerial images can significantly reduce matching ambiguity between these two views, especially when the ground-level images have a limited Field of View (FoV) instead of a full field-of-view panorama. Therefore, we design a Dynamic Similarity Matching network to estimate cross-view orientation alignment during localization. In particular, we address the cross-view domain gap by applying a polar transform to the aerial images to approximately align the images up to an unknown azimuth angle. Then, a two-stream convolutional network is used to learn deep features from the ground and polar-transformed aerial images. Finally, we obtain the orientation by computing the correlation between cross-view features, which also provides a more accurate measure of feature similarity, improving location recall. Experiments on standard datasets demonstrate that our method significantly improves state-of-the-art performance. Remarkably, we improve the top-1 location recall rate on the CVUSA dataset by a factor of 1.5× for panoramas with known orientation, by a factor of 3.3× for panoramas with unknown orientation, and by a factor of 6× for 180 -FoV images with unknown orientation. ◦

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http://hdl.handle.net/10453/148151