Automated Re-Identification of Holstein-Friesian Cattle in Dense Crowds

• URL: http://arxiv.org/abs/2602.15962v1
• Date: Tue, 17 Feb 2026 19:25:50 GMT
• Title: Automated Re-Identification of Holstein-Friesian Cattle in Dense Crowds
• Authors: Phoenix Yu, Tilo Burghardt, Andrew W Dowsey, Neill W Campbell,
• Abstract summary: We propose a new detect-segment-identify pipeline that leverages the Open-Vocabulary Weight-free Localisation and the Segment Anything models.<n>Our methodology overcomes detection breakdown in dense animal groupings, resulting in a 98.93% accuracy.<n>We show that unsupervised contrastive learning can build on this to yield 94.82% Re-ID accuracy on our test data.
• Score: 2.3843187053931456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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