Abstract
Given a picture taken somewhere in the world, automatic geo-localization of such an image is an extremely useful task especially for historical and forensic sciences, documentation purposes, organization of the world’s photographs and intelligence applications. While tremendous progress has been made over the last years in visual location recognition within a single city, localization in natural environments is much more difficult, since vegetation, illumination, seasonal changes make appearance-only approaches impractical. In this chapter, we target mountainous terrain and use digital elevation models to extract representations for fast visual database lookup. We propose an automated approach for very large-scale visual localization that can efficiently exploit visual information (contours) and geometric constraints (consistent orientation) at the same time. We validate the system at the scale of Switzerland (\(40\,000\,\mathrm {km}^2\)) using over 1000 landscape query images with ground truth GPS position.
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Notes
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A preliminary version of this system has been presented in [2].
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Synthetic experiments verified that taking the photo from 10 or 50 m above the ground does not degrade recognition besides very special cases like standing very close to a small wall.
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Acknowledgments
This work has been supported through SNF grant 127224 by the Swiss National Science Foundation. We also thank Simon Wenner for his help to render the DEMs and Hiroto Nagayoshi for providing the CH2 dataset.
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Saurer, O., Baatz, G., Köser, K., Ladický, L., Pollefeys, M. (2016). Image-Based Large-Scale Geo-localization in Mountainous Regions. In: Zamir, A., Hakeem, A., Van Gool, L., Shah, M., Szeliski, R. (eds) Large-Scale Visual Geo-Localization. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-25781-5_11
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