A new CorrNet designed for unaligned cross-view positioning

Shujuan Fan

doi:10.1117/12.2626434

20 December 2021 A new CorrNet designed for unaligned cross-view positioning

Shujuan Fan

Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 121550W (2021) https://doi.org/10.1117/12.2626434
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Abstract

At present, the related work of ground-to-air geolocation is mainly focused on alignment, that is, the direction of the street view image is accurately aligned with the corresponding satellite image. However, the orientation of the street view image and the aerial image cannot be exactly aligned in real life. In this work, we first studied the problem of unaligned ground-to-air positioning. Since there is no published cross-view dataset with unaligned directions, this work processes the CVUSA dataset to generate a unaligned cross-view dataset. This work introduces correlation layer modules in the cross-view positioning task for the first time. We also design a new regression network to estimate the similarity of the two views and design a triple loss function based on the similarity.

Citation Download Citation

Shujuan Fan "A new CorrNet designed for unaligned cross-view positioning", Proc. SPIE 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021), 121550W (20 December 2021); https://doi.org/10.1117/12.2626434

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