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Analysis of Dogs’ Sleep Patterns Using Convolutional Neural Networks

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Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11729))

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Abstract

Video-based analysis is one of the most important tools of animal behavior and animal welfare scientists. While automatic analysis systems exist for many species, this problem has not yet been adequately addressed for one of the most studied species in animal science—dogs. In this paper we describe a system developed for analyzing sleeping patterns of kenneled dogs, which may serve as indicator of their welfare. The system combines convolutional neural networks with classical data processing methods, and works with very low quality video from cameras installed in dogs shelters.

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Notes

  1. 1.

    See: https://www.fitbark.com/.

  2. 2.

    See: https://www.whistle.com/.

  3. 3.

    See: https://petpace.com/.

  4. 4.

    The study was approved by the ethical panels of both institutions; protocol numbers: University of Salford Ethical Approval Panel - STR1617-80, CEUA/UFOP (Brazil) - 2017/04.

  5. 5.

    It should be noted that the chosen end-to-end architecture has a drawback of simultaneous detection of the same dog as sleeping and awake due to its detection of two objects (sleeping and awake dog) independently. However, this happens in very rare cases and can be overcome by using a higher confidence level for classification.

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Acknowledgement

This work has been supported by the NVIDIA GPU grant program.

Author information

Authors and Affiliations

  1. Information Systems Department, University of Haifa, Haifa, Israel

    Anna Zamansky & Michael Plazner

  2. Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

    Aleksandr M. Sinitca & Dmitry I. Kaplun

  3. School of Environmental and Life Sciences, University of Salford, Greater Manchester, UK

    Ivana G. Schork & Robert J. Young

  4. Department of Biodiversity, Evolution and Environment, Federal University of Ouro Preto, Ouro Preto, Brazil

    Cristiano S. de Azevedo

Authors
  1. Anna Zamansky
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  2. Aleksandr M. Sinitca
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  3. Dmitry I. Kaplun
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  4. Michael Plazner
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  5. Ivana G. Schork
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  6. Robert J. Young
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  7. Cristiano S. de Azevedo
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Corresponding author

Correspondence to Anna Zamansky .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Praha 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Zamansky, A. et al. (2019). Analysis of Dogs’ Sleep Patterns Using Convolutional Neural Networks. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_38

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  • DOI: https://doi.org/10.1007/978-3-030-30508-6_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30507-9

  • Online ISBN: 978-3-030-30508-6

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