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^*

^*Corresponding author for this work

Computer Information Sciences Department

Research output: Contribution to journal › Conference article › peer-review

14 Citations (Scopus)

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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
Pages (from-to)	661-668
Number of pages	8
Journal	Procedia Computer Science
Volume	186
Early online date	11 Jun 2021
DOIs	https://doi.org/10.1016/j.procs.2021.04.187
Publication status	Published - 2021
Event	14th International Symposium on Intelligent Systems, INTELS 2020 - Moscow, Russian Federation Duration: 14 Dec 2020 → 16 Dec 2020

Keywords

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

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10.1016/j.procs.2021.04.187Licence: CC BY-NC-ND

1-s2.0-S1877050921010267-mainFinal published version, 952 KBLicence: CC BY-NC-ND

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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 {van der Linden}, Dirk 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.; 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",

pages = "661--668",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier",

}

TY - JOUR

T1 - Automatic Animal Behavior Analysis

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

AU - Zamansky, Anna

AU - Sinitca, Aleksandr

AU - van der Linden, Dirk

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.

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 article

AN - SCOPUS:85112725074

SN - 1877-0509

VL - 186

SP - 661

EP - 668

JO - Procedia Computer Science

JF - Procedia Computer Science

Y2 - 14 December 2020 through 16 December 2020

ER -

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

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