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International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 582–592Cite as

Change Detection and Object Recognition Using Aerial Robots

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

Abstract

This work proposes a strategy for autonomous change detection and classification using aerial robots. For aerial robotic missions that were conducted in different spatio–temporal conditions, the pose–annotated camera data are first compared for similarity in order to identify the correspondence map among the different image sets. Then efficient feature matching techniques relying on binary descriptors are used to estimate the geometric transformations among the corresponding images, and subsequently perform image subtraction and filtering to robustly detect change. To further decrease the computational load, the known poses of the images are used to create local subsets within which similar images are expected to be found. Once change detection is accomplished, a small set of the images that present the maximum levels of change are used to classify the change by searching to recognize a list of known objects through a bag–of–features approach. The proposed algorithm is evaluated using both handheld–smartphone collected data, as well as experiments using an aerial robot.

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Author information

Authors and Affiliations

  1. Autonomous Robots Lab, University of Nevada, Reno, Reno, Nevada, USA

    Shehryar Khattak, Christos Papachristos & Kostas Alexis

Authors
  1. Shehryar Khattak
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  2. Christos Papachristos
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  3. Kostas Alexis
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Corresponding author

Correspondence to Kostas Alexis .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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Cite this paper

Khattak, S., Papachristos, C., Alexis, K. (2016). Change Detection and Object Recognition Using Aerial Robots. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_52

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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