Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning

Anna Zamansky; Aleksandr Sinitca; Dirk Van Der Linden; Dmitry Kaplun

doi:10.1016/j.procs.2021.04.187

Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning

Anna Zamansky, Aleksandr Sinitca, Dirk Van Der Linden, Dmitry Kaplun

Department of Information Systems

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

Abstract

Computational animal behavior analysis (CABA) is an emerging field which aims to apply AI techniques to support animal behavior analysis. The need for computational approaches which facilitate 'objectivization' and quantification of behavioral characteristics of animals is widely acknowledged within several animal-related scientific disciplines. State-of-the-art CABA approaches mainly apply machine learning (ML) techniques, combining it with approaches from computer vision and IoT. In this paper we highlight the potential applications of integrating knowledge representation approaches in the context of ML-based CABA systems, demonstrating the ideas using insights from an ongoing CABA project.

Original language	English
Title of host publication	14th International Symposium on Intelligent Systems, INTELS 2020
Pages	661-668
Number of pages	8
Volume	186
DOIs	https://doi.org/10.1016/j.procs.2021.04.187
State	Published - 2021
Event	14th International Symposium on Intelligent Systems, INTELS 2020 - Moscow, Russian Federation Duration: 14 Dec 2020 → 16 Dec 2020

Publication series

Name	Procedia Computer Science
Publisher	Elsevier BV
ISSN (Print)	1877-0509

Conference

Conference	14th International Symposium on Intelligent Systems, INTELS 2020
Country/Territory	Russian Federation
City	Moscow
Period	14/12/20 → 16/12/20

Bibliographical note

Funding Information:
This research was supported by a grant from the Ministry of Science and Technology of Israel and by RFBR according to the research project N 19-57-06007.

Publisher Copyright:
© 2021 Elsevier B.V.. All rights reserved.

Keywords

Animal Behaviour
Artificial Intelligence
Computational Analysis
Computer Vision
Machine Learning
Spatio-temporal Data Processing

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1016/j.procs.2021.04.187

Cite this

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title = "Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning",

abstract = "Computational animal behavior analysis (CABA) is an emerging field which aims to apply AI techniques to support animal behavior analysis. The need for computational approaches which facilitate 'objectivization' and quantification of behavioral characteristics of animals is widely acknowledged within several animal-related scientific disciplines. State-of-the-art CABA approaches mainly apply machine learning (ML) techniques, combining it with approaches from computer vision and IoT. In this paper we highlight the potential applications of integrating knowledge representation approaches in the context of ML-based CABA systems, demonstrating the ideas using insights from an ongoing CABA project.",

keywords = "Animal Behaviour, Artificial Intelligence, Computational Analysis, Computer Vision, Machine Learning, Spatio-temporal Data Processing",

author = "Anna Zamansky and Aleksandr Sinitca and Linden, {Dirk Van Der} and Dmitry Kaplun",

note = "Funding Information: This research was supported by a grant from the Ministry of Science and Technology of Israel and by RFBR according to the research project N 19-57-06007. Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.. All rights reserved.; 14th International Symposium on Intelligent Systems, INTELS 2020 ; Conference date: 14-12-2020 Through 16-12-2020",

year = "2021",

doi = "10.1016/j.procs.2021.04.187",

language = "English",

volume = "186",

series = "Procedia Computer Science",

publisher = "Elsevier BV",

pages = "661--668",

booktitle = "14th International Symposium on Intelligent Systems, INTELS 2020",

}

Zamansky, A, Sinitca, A, Linden, DVD & Kaplun, D 2021, Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning. in 14th International Symposium on Intelligent Systems, INTELS 2020. vol. 186, Procedia Computer Science, pp. 661-668, 14th International Symposium on Intelligent Systems, INTELS 2020, Moscow, Russian Federation, 14/12/20. https://doi.org/10.1016/j.procs.2021.04.187

Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning. / Zamansky, Anna; Sinitca, Aleksandr; Linden, Dirk Van Der et al.
14th International Symposium on Intelligent Systems, INTELS 2020. Vol. 186 2021. p. 661-668 (Procedia Computer Science).

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

TY - GEN

T1 - Automatic Animal Behavior Analysis

T2 - 14th International Symposium on Intelligent Systems, INTELS 2020

AU - Zamansky, Anna

AU - Sinitca, Aleksandr

AU - Linden, Dirk Van Der

AU - Kaplun, Dmitry

N1 - Funding Information: This research was supported by a grant from the Ministry of Science and Technology of Israel and by RFBR according to the research project N 19-57-06007. Publisher Copyright: © 2021 Elsevier B.V.. All rights reserved.

PY - 2021

Y1 - 2021

N2 - Computational animal behavior analysis (CABA) is an emerging field which aims to apply AI techniques to support animal behavior analysis. The need for computational approaches which facilitate 'objectivization' and quantification of behavioral characteristics of animals is widely acknowledged within several animal-related scientific disciplines. State-of-the-art CABA approaches mainly apply machine learning (ML) techniques, combining it with approaches from computer vision and IoT. In this paper we highlight the potential applications of integrating knowledge representation approaches in the context of ML-based CABA systems, demonstrating the ideas using insights from an ongoing CABA project.

AB - Computational animal behavior analysis (CABA) is an emerging field which aims to apply AI techniques to support animal behavior analysis. The need for computational approaches which facilitate 'objectivization' and quantification of behavioral characteristics of animals is widely acknowledged within several animal-related scientific disciplines. State-of-the-art CABA approaches mainly apply machine learning (ML) techniques, combining it with approaches from computer vision and IoT. In this paper we highlight the potential applications of integrating knowledge representation approaches in the context of ML-based CABA systems, demonstrating the ideas using insights from an ongoing CABA project.

KW - Animal Behaviour

KW - Artificial Intelligence

KW - Computational Analysis

KW - Computer Vision

KW - Machine Learning

KW - Spatio-temporal Data Processing

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U2 - 10.1016/j.procs.2021.04.187

DO - 10.1016/j.procs.2021.04.187

M3 - Conference contribution

AN - SCOPUS:85112725074

VL - 186

T3 - Procedia Computer Science

SP - 661

EP - 668

BT - 14th International Symposium on Intelligent Systems, INTELS 2020

Y2 - 14 December 2020 through 16 December 2020

ER -

Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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