Holstein-Friesian detection and re-identification (Re-ID) methods capture individuals well when targets are spatially separate. However, existing approaches, including YOLO-based species detection, break down when cows group closely together. This is particularly prevalent for species which have outline-breaking coat patterns. To boost both effectiveness and transferability in this setting, we propose a new detect-segment-identify pipeline that leverages the Open-Vocabulary Weight-free Localisation and the Segment Anything models as pre-processing stages alongside Re-ID networks. To evaluate our approach, we publish a collection of nine days CCTV data filmed on a working dairy farm. Our methodology overcomes detection breakdown in dense animal groupings, resulting in a 98.93% accuracy. This significantly outperforms current oriented bounding box-driven, as well as SAM species detection baselines with accuracy improvements of 47.52% and 27.13%, respectively. We show that unsupervised contrastive learning can build on this to yield 94.82% Re-ID accuracy on our test data. Our work demonstrates that Re-ID in crowded scenarios is both practical as well as reliable in working farm settings with no manual intervention. Code and dataset are provided for reproducibility.

翻译：荷斯坦牛的检测与重识别方法在目标空间分离时能很好地捕获个体。然而，当奶牛紧密聚集时，包括基于YOLO的物种检测在内的现有方法会失效。这对于具有轮廓破坏性被毛图案的物种尤为普遍。为在此场景下提升效能与可迁移性，我们提出一种新的检测-分割-识别流程，该流程利用开放词汇无权重定位模型与Segment Anything模型作为预处理阶段，并结合重识别网络。为评估我们的方法，我们发布了一个在工作奶牛场拍摄的九天闭路电视数据集合。我们的方法克服了密集动物群体中的检测失效问题，实现了98.93%的准确率。这显著优于当前基于定向边界框驱动的方法以及SAM物种检测基线，准确率分别提升了47.52%和27.13%。我们证明，无监督对比学习可以在此基础上，在我们的测试数据上实现94.82%的重识别准确率。我们的工作表明，在无需人工干预的工作农场环境中，拥挤场景下的重识别既实用又可靠。我们提供了代码和数据集以确保可复现性。