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A Gaussian Mixture Model Feature for Wildlife Detection

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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

This paper addresses the challenge of the camouflage in the wildlife detection. The protective coloring makes the color space distance between the features of animal pattern and background pattern very small. The texture information should be considered under this situation. A reliable differential estimator for digital image data is employed. The estimation of the first order and the second order differentials of animal and background patterns are modelled using Gaussian mixture model method. It is shown the animal and background have bigger distance in Gaussian mixture models than in the color space. The mathematical expectation and standard deviation of the Gaussian models are therefore used to build the features to represent animal and background patterns. To demonstrate the performance of the proposed features, a neural network classifier is employed. The experiment results on wildlife scene images show that the proposed features have high classifying capacity to detect animals with camouflage from the background environment.

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Mechatronics and Industrial Design, Tshwane University of Technology, Pretoria, 0001, South Africa

    Shengzhi Du & Barend Jacobus van Wyk

  2. Department of Computer Systems Engineering, Tshwane University of Technology, Pretoria, 0001, South Africa

    Chunling Du

  3. Melbourne Institute of Technology, Melbourne, Australia

    Rishaad Abdoola

Authors
  1. Shengzhi Du
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  2. Chunling Du
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  3. Rishaad Abdoola
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  4. Barend Jacobus van Wyk
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Corresponding author

Correspondence to Shengzhi Du .

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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Du, S., Du, C., Abdoola, R., van Wyk, B.J. (2016). A Gaussian Mixture Model Feature for Wildlife Detection. 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_68

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_68

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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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