用视觉系统自动识别马厩中马和人的行为事件，提升动物福利监测效率。
Monitoring Horses in Stalls: From Object to Event Detection
- 结合YOLOv11与BoT-SORT实现马匹与人员的实时检测跟踪
- 可识别五类行为事件，准确处理摄像头盲区问题
- 适合马场管理、动物行为研究者使用
监控马厩中马匹的行为对早期发现健康与福利问题至关重要，但传统方法依赖人工，耗时费力。本文提出一个基于视觉的原型监测系统，利用目标检测与多目标追踪技术自动识别并跟踪马厩内马匹与人员。系统采用YOLOv11进行检测，BoT-SORT实现跟踪，并基于物体轨迹与马厩内空间关系推断事件状态。为支持开发，构建了自定义数据集，借助CLIP与GroundingDINO基础模型辅助标注。系统可区分五类事件，有效应对摄像头视野盲区。定性评估显示对马匹相关事件具备可靠性能，但因数据不足，对人员检测仍存局限。本工作为马厩环境中的实时行为监测提供了基础，对动物福利与饲养管理具有重要意义。
原文摘要 · Abstract (English)
Monitoring the behavior of stalled horses is essential for early detection of health and welfare issues but remains labor-intensive and time-consuming. In this study, we present a prototype vision-based monitoring system that automates the detection and tracking of horses and people inside stables using object detection and multi-object tracking techniques. The system leverages YOLOv11 and BoT-SORT for detection and tracking, while event states are inferred based on object trajectories and spatial relations within the stall. To support development, we constructed a custom dataset annotated with assistance from foundation models CLIP and GroundingDINO. The system distinguishes between five event types and accounts for the camera's blind spots. Qualitative evaluation demonstrated reliable performance for horse-related events, while highlighting limitations in detecting people due to data scarcity. This work provides a foundation for real-time behavioral monitoring in equine facilities, with implications for animal welfare and stable management.
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