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Semantically Guided Geo-location and Modeling in Urban Environments

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Book cover Large-Scale Visual Geo-Localization

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

The problem of localization and geo-location estimation of an image has a long-standing history both in robotics and computer vision. With the advent of availability of large amounts of geo-referenced image data, several image retrieval approaches have been deployed to tackle this problem. In this work, we will show how the capability of semantic labeling of both query views and the reference dataset by means of semantic segmentation can aid (1) the problem of retrieval of views similar and possibly overlapping with the query and (2) guide the recognition and discovery of commonly occurring scene layouts in the reference dataset. We will demonstrate the effectiveness of these semantic representations on examples of localization, semantic concept discovery, and intersection recognition in the images of urban scenes.

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Acknowledgments

Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory, contract FA8650-12-C-7212. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL, or the U.S. Government.

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Authors and Affiliations

  1. George Mason University, Fairfax, VA, USA

    Gautam Singh & Jana Košecká

Authors
  1. Gautam Singh
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  2. Jana Košecká
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Corresponding author

Correspondence to Gautam Singh .

Editor information

Editors and Affiliations

  1. Computer Science Department, Stanford University Computer Science Department, Stanford, California, USA

    Amir R. Zamir

  2. Decisive Analytics Corporation, Arlington, Virginia, USA

    Asaad Hakeem

  3. ETH Zürich, Zürich, Switzerland

    Luc Van Gool

  4. University of Central Florida, Orlando, Florida, USA

    Mubarak Shah

  5. Facebook, Seattle, Washington, USA

    Richard Szeliski

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© 2016 Springer International Publishing Switzerland

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Singh, G., Košecká, J. (2016). Semantically Guided Geo-location and Modeling in Urban Environments. 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_6

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  • DOI: https://doi.org/10.1007/978-3-319-25781-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25779-2

  • Online ISBN: 978-3-319-25781-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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