Monitoring the health conditions of fish is essential, as it enables the early detection of disease, safeguards animal welfare, and contributes to sustainable aquaculture practices. Physiological and pathological conditions of cultivated fish can be inferred by analyzing locomotion activities. In this paper, we present a system that estimates the locomotion activities from videos using multi object tracking. The core of our approach is a YOLOv11 detector embedded in a tracking-by-detection framework. We investigate various configurations of the YOLOv11-architecture as well as extensions that incorporate multiple frames to improve detection accuracy. Our system is evaluated on a manually annotated dataset of Sulawesi ricefish recorded in a home-aquarium-like setup, demonstrating its ability to reliably measure swimming direction and speed for fish health monitoring. The dataset will be made publicly available upon publication.

翻译：监测鱼类健康状况至关重要，因为它能够实现疾病的早期检测、保障动物福利，并促进可持续水产养殖实践。养殖鱼类的生理和病理状况可通过分析其运动活动进行推断。本文提出一种利用多目标跟踪从视频中估计运动活动的系统。我们方法的核心是嵌入检测跟踪框架的YOLOv11检测器。我们研究了YOLOv11架构的多种配置，以及融合多帧信息以提高检测精度的扩展方案。该系统在家庭水族箱环境中录制的苏拉威西米鱼人工标注数据集上进行评估，证明了其能够可靠测量游动方向与速度，以支持鱼类健康监测。该数据集将在论文发表后公开提供。