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

Publication Type:
Conference Proceeding
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, 00, pp. 4063-4071
Issue Date:
Filename Description Size
09157033.pdfPublished version1.06 MB
Adobe PDF
Full metadata record
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. ◦
Please use this identifier to cite or link to this item: