Analysis of dogs’ sleep patterns using convolutional neural networks

Anna Zamansky; Aleksandr M. Sinitca; Dmitry I. Kaplun; Michael Plazner; Ivana G. Schork; Robert J. Young; Cristiano S. de Azevedo

doi:10.1007/978-3-030-30508-6_38

Analysis of dogs’ sleep patterns using convolutional neural networks

Anna Zamansky, Aleksandr M. Sinitca, Dmitry I. Kaplun, Michael Plazner, Ivana G. Schork, Robert J. Young, Cristiano S. de Azevedo

Department of Information Systems

University of Haifa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	American English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2019
Subtitle of host publication	Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings
Editors	Igor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
Publisher	Springer Verlag
Pages	472-483
Number of pages	12
ISBN (Print)	9783030305079
DOIs	https://doi.org/10.1007/978-3-030-30508-6_38
State	Published - 2019
Event	28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany Duration: 17 Sep 2019 → 19 Sep 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11729 LNCS

Conference

Conference	28th International Conference on Artificial Neural Networks, ICANN 2019
Country/Territory	Germany
City	Munich
Period	17/09/19 → 19/09/19

Keywords

Animal science
Animal welfare
Computer vision
Convolutional neural networks

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-30508-6_38

Cite this

Zamansky, A., Sinitca, A. M., Kaplun, D. I., Plazner, M., Schork, I. G., Young, R. J., & de Azevedo, C. S. (2019). Analysis of dogs’ sleep patterns using convolutional neural networks. In I. V. Tetko, P. Karpov, F. Theis, & V. Kurková (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings (pp. 472-483). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11729 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-30508-6_38

Zamansky, Anna ; Sinitca, Aleksandr M. ; Kaplun, Dmitry I. et al. / Analysis of dogs’ sleep patterns using convolutional neural networks. Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. editor / Igor V. Tetko ; Pavel Karpov ; Fabian Theis ; Vera Kurková. Springer Verlag, 2019. pp. 472-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Analysis of dogs{\textquoteright} sleep patterns using convolutional neural networks",

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.",

keywords = "Animal science, Animal welfare, Computer vision, Convolutional neural networks",

author = "Anna Zamansky and Sinitca, {Aleksandr M.} and Kaplun, {Dmitry I.} and Michael Plazner and Schork, {Ivana G.} and Young, {Robert J.} and {de Azevedo}, {Cristiano S.}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 28th International Conference on Artificial Neural Networks, ICANN 2019 ; Conference date: 17-09-2019 Through 19-09-2019",

year = "2019",

doi = "10.1007/978-3-030-30508-6_38",

language = "American English",

isbn = "9783030305079",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "472--483",

editor = "Tetko, {Igor V.} and Pavel Karpov and Fabian Theis and Vera Kurkov{\'a}",

booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2019",

address = "Germany",

}

Zamansky, A, Sinitca, AM, Kaplun, DI, Plazner, M, Schork, IG, Young, RJ & de Azevedo, CS 2019, Analysis of dogs’ sleep patterns using convolutional neural networks. in IV Tetko, P Karpov, F Theis & V Kurková (eds), Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11729 LNCS, Springer Verlag, pp. 472-483, 28th International Conference on Artificial Neural Networks, ICANN 2019, Munich, Germany, 17/09/19. https://doi.org/10.1007/978-3-030-30508-6_38

Analysis of dogs’ sleep patterns using convolutional neural networks. / Zamansky, Anna; Sinitca, Aleksandr M.; Kaplun, Dmitry I. et al.
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. ed. / Igor V. Tetko; Pavel Karpov; Fabian Theis; Vera Kurková. Springer Verlag, 2019. p. 472-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11729 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Analysis of dogs’ sleep patterns using convolutional neural networks

AU - Zamansky, Anna

AU - Sinitca, Aleksandr M.

AU - Kaplun, Dmitry I.

AU - Plazner, Michael

AU - Schork, Ivana G.

AU - Young, Robert J.

AU - de Azevedo, Cristiano S.

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Animal science

KW - Animal welfare

KW - Computer vision

KW - Convolutional neural networks

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U2 - 10.1007/978-3-030-30508-6_38

DO - 10.1007/978-3-030-30508-6_38

M3 - Conference contribution

SN - 9783030305079

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 472

EP - 483

BT - Artificial Neural Networks and Machine Learning – ICANN 2019

A2 - Tetko, Igor V.

A2 - Karpov, Pavel

A2 - Theis, Fabian

A2 - Kurková, Vera

PB - Springer Verlag

T2 - 28th International Conference on Artificial Neural Networks, ICANN 2019

Y2 - 17 September 2019 through 19 September 2019

ER -

Zamansky A, Sinitca AM, Kaplun DI, Plazner M, Schork IG, Young RJ et al. Analysis of dogs’ sleep patterns using convolutional neural networks. In Tetko IV, Karpov P, Theis F, Kurková V, editors, Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. Springer Verlag. 2019. p. 472-483. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-30508-6_38

Analysis of dogs’ sleep patterns using convolutional neural networks

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Keywords

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