Abstract
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.
Abstract (translated)
监测鱼类的健康状况至关重要，因为它能够实现疾病的早期检测、保障动物福利，并促进可持续的水产养殖实践。通过分析鱼的行为活动可以推断出养殖鱼类的生理和病理状态。本文介绍了一种利用多目标跟踪技术从视频中估算行为活动的系统。我们的方法核心是一个嵌入到基于检测的追踪框架中的YOLOv11探测器。我们研究了不同配置的YOLOv11架构，并且通过整合多个帧来提高检测精度，探讨了其扩展性。 该系统在手动标注的数据集上进行了评估，数据集中包含在类似家庭水族箱环境中录制的苏拉威西稻鱼的行为记录。结果显示，本系统能够可靠地测量鱼类游泳的方向和速度，从而用于监测鱼类健康状况。在论文发表后，此数据集将公开发布供研究使用。
URL
https://arxiv.org/abs/2603.05407
