@inproceedings{dae71091c902425f8672622868acb811,
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 = "https://doi.org/10.1016/j.procs.2021.04.187",
language = "American English",
volume = "186",
series = "Procedia Computer Science",
publisher = "Elsevier BV",
pages = "661--668",
booktitle = "14th International Symposium on Intelligent Systems, INTELS 2020",
}