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.
|Number of pages||8|
|Journal||Procedia Computer Science|
|Early online date||11 Jun 2021|
|Publication status||Published - 2021|
|Event||14th International Symposium on Intelligent Systems, INTELS 2020 - Moscow, Russian Federation|
Duration: 14 Dec 2020 → 16 Dec 2020