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

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)
9 Downloads (Pure)

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 languageEnglish
Pages (from-to)661-668
Number of pages8
JournalProcedia Computer Science
Volume186
Early online date11 Jun 2021
DOIs
Publication statusPublished - 2021
Event14th International Symposium on Intelligent Systems, INTELS 2020 - Moscow, Russian Federation
Duration: 14 Dec 202016 Dec 2020

Fingerprint

Dive into the research topics of 'Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning'. Together they form a unique fingerprint.

Cite this