We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their unique black and white coat-patterns. Captured by three ceiling-mounted visual sensors covering adjacent barn areas over seven days on a working dairy farm, the dataset comprises 101, 329 images of 90 cows, plus the underlying original CCTV footage. The dataset is provided alongside full computer vision recognition baselines, that is both a supervised and self-supervised learning framework for individual cow identification trained on cattle tracklets. We report a performance above 96% single image identification accuracy from the dataset and demonstrate that combining data from multiple cameras during learning enhances self-supervised identification. We show that our framework enables fully automatic cattle identification, barring only the simple human verification of tracklet integrity during data collection. Crucially, our study highlights that multi-camera, supervised and self-supervised components in tandem not only deliver highly accurate individual cow identification but also achieve this efficiently with no labelling of cattle identities by humans at all. We argue that this improvement in efficacy has practical implications for livestock management, behaviour analysis, and agricultural monitoring. For full reproducibility and practical ease of use, we publish all key software and code including re-identification components and the species detector with this paper.

本文提出了MultiCamCows2024，一个跨多相机拍摄的农场规模图像数据集，用于单个荷斯坦-弗里斯牛的生物识别。该数据集实现了超过96%的单图像识别准确率，且通过多相机数据结合的学习方法，显著提升了自监督识别的效果，具有深远的农业管理和行为分析影响。

MultiCamCows2024：用于农场工作中基于人工智能的荷斯坦-弗里斯牛再识别的多视角图像数据集