  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.
