用视频分析鱼的游动行为，实现健康状态早期监测。
Video-based Locomotion Analysis for Fish Health Monitoring
- 基于YOLOv11的多目标追踪框架，结合多帧信息提升检测精度。
- 在苏拉威西稻鱼数据集上准确测量游泳方向与速度。
- 适合水产养殖健康监测、动物行为研究者使用。
鱼类健康监测至关重要，可实现疾病早期发现、保障动物福利并促进可持续水产养殖。通过分析鱼类运动行为，可推断其生理与病理状态。本文提出一种基于视频的运动活动估计系统，采用嵌入在检测-追踪框架中的YOLOv11检测器。我们探究了YOLOv11架构的不同配置及引入多帧信息的扩展方法以提升检测准确性。系统在人工标注的苏拉威西稻鱼数据集上进行评估，该数据集在家庭水族箱环境下录制，结果表明系统能可靠地测量鱼的游泳方向与速度，适用于鱼类健康监测。数据集将在论文发表后公开。
原文摘要 · Abstract (English)
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.
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