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Fast, Deep Detection and Tracking of Birds and Nests

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Advances in Visual Computing (ISVC 2016)

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

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Abstract

We present a visual object detector based on a deep convolutional neural network that quickly outputs bounding box hypotheses without a separate proposal generation stage [1]. We modify the network for better performance, specialize it for a robotic application involving “bird” and “nest” categories (including the creation of a new dataset for the latter), and extend it to enforce temporal continuity for tracking. The system exhibits very competitive detection accuracy and speed, as well as robust, high-speed tracking on several difficult sequences.

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Notes

  1. 1.

    Full nest dataset available here: http://nameless.cis.udel.edu/data/nests.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA

    Qiaosong Wang, Christopher Rasmussen & Chunbo Song

Authors
  1. Qiaosong Wang
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  2. Christopher Rasmussen
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  3. Chunbo Song
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Corresponding author

Correspondence to Christopher Rasmussen .

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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Wang, Q., Rasmussen, C., Song, C. (2016). Fast, Deep Detection and Tracking of Birds and Nests. 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_14

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_14

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  • 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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