Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification

TL;DR

This paper presents a highly efficient method for identifying individual Holstein-Friesian cattle using minimal manual labeling, combining self-supervision, clustering, and active learning to achieve over 92% accuracy on farm CCTV footage.

Contribution

The study introduces a novel, minimal-labelling approach that effectively integrates self-supervised learning, clustering, and active learning for cattle identification in real-world farm environments.

Findings

Achieved 92.44% identification accuracy with minimal manual labeling.

Reduced annotation effort from traditional methods by leveraging self-supervision and active learning.

Demonstrated effectiveness on the publicly available Cows2021 dataset.

Abstract

We describe a practically evaluated approach for training visual cattle ID systems for a whole farm requiring only ten minutes of labelling effort. In particular, for the task of automatic identification of individual Holstein-Friesians in real-world farm CCTV, we show that self-supervision, metric learning, cluster analysis, and active learning can complement each other to significantly reduce the annotation requirements usually needed to train cattle identification frameworks. Evaluating the approach on the test portion of the publicly available Cows2021 dataset, for training we use 23,350 frames across 435 single individual tracklets generated by automated oriented cattle detection and tracking in operational farm footage. Self-supervised metric learning is first employed to initialise a candidate identity space where each tracklet is considered a distinct entity. Grouping entities into equivalence classes representing cattle identities is then performed by automated merging via cluster analysis and active learning. Critically, we identify the inflection point at which automated choices cannot replicate improvements based on human intervention to reduce annotation to a minimum. Experimental results show that cluster analysis and a few minutes of labelling after automated self-supervision can improve the test identification accuracy of 153 identities to 92.44% (ARI=0.93) from the 74.9% (ARI=0.754) obtained by self-supervision only. These promising results indicate that a tailored combination of human and machine reasoning in visual cattle ID pipelines can be highly effective whilst requiring only minimal labelling effort. We provide all key source code and network weights with this paper for easy result reproduction.