Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching

• URL: http://arxiv.org/abs/2005.03860v1
• Date: Fri, 8 May 2020 05:21:16 GMT
• Title: Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching
• Authors: Yujiao Shi, Xin Yu, Dylan Campbell, Hongdong Li
• Abstract summary: Cross-view geo-localization is a problem given a large-scale database of geo-tagged aerial images. Knowing orientation between ground and aerial images can significantly reduce matching ambiguity between these two views. We design a Dynamic Similarity Matching network to estimate cross-view orientation alignment during localization.
• Score: 95.64702426906466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 1.5x for panoramas with known orientation, by a factor of 3.3x for panoramas with unknown orientation, and by a factor of 6x for 180-degree FoV images with unknown orientation.

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