Abstract
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.
Abstract (translated)
监测停滞马的行为对于早期发现健康和福利问题至关重要，但这项工作既耗时又费力。在本研究中，我们提出了一种基于视觉的监控系统原型，该系统利用目标检测和多对象跟踪技术自动检测并追踪马匹及人员在马厩内的行为。此系统采用YOLOv11和BoT-SORT进行检测与跟踪，并根据物体轨迹和马厩内部的空间关系推断事件状态。为了支持系统的开发，我们构建了一个使用CLIP和GroundingDINO基础模型辅助注释的自定义数据集。该系统能够区分五种不同的事件类型，并考虑到摄像头盲区的影响。 定性评估表明，在与马匹相关的事件上，该系统表现可靠，但在因数据不足而难以检测人员时也存在局限性。这项工作为实现赛马设施中的实时行为监控奠定了基础，对动物福利和马厩管理具有重要意义。
URL
https://arxiv.org/abs/2510.17409
