Abstract
Computer vision models such as You Only Look Once (YOLO) excel at tracking general objects, yet they often struggle to accurately track multiple animals. In this study, we aimed to verify the improvement of animal tracking performance using YOLO by fine-tuning. We obtained videos of multiple domestic cats by ourselves and annotated the videos, and performed fine-tuning with this video dataset. Results of this study show that even training with less than one hundred images improves accuracy and enables tracking of individual animals. This result suggests that our method is promising for the realization of animal social behavior analysis.
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