Paper Info
Title
Where Am I Looking At? Joint Location And Orientation Estimation By Cross-View Matching
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-I location recall rate on the CVUSA dataset by a factor of 1.5 x for panoramas with known orientation, by a factor of 3.3 x for panoramas with unknown orientation, and by a factor of 6 x for 180 degrees -FoV images with unknown orientation.
Year
DOI
Venue
2020
10.1109/CVPR42600.2020.00412
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
DocType
ISSN
Citations 
Conference
1063-6919
4
PageRank 
References 
Authors
0.38
17
4
Name
Order
Citations
PageRank
Yujiao Shi1273.03
Xin Yu221228.98
D. J. Campbell31458.47
Hongdong Li41724101.81