  In this paper we publish the largest identity-annotated Holstein-Friesian
cattle dataset Cows2021 and a first self-supervision framework for video
identification of individual animals. The dataset contains 10,402 RGB images
with labels for localisation and identity as well as 301 videos from the same
herd. The data shows top-down in-barn imagery, which captures the breed's
individually distinctive black and white coat pattern. Motivated by the
labelling burden involved in constructing visual cattle identification systems,
we propose exploiting the temporal coat pattern appearance across videos as a
self-supervision signal for animal identity learning. Using an
individual-agnostic cattle detector that yields oriented bounding-boxes,
rotation-normalised tracklets of individuals are formed via
tracking-by-detection and enriched via augmentations. This produces a
`positive' sample set per tracklet, which is paired against a `negative' set
sampled from random cattle of other videos. Frame-triplet contrastive learning
is then employed to construct a metric latent space. The fitting of a Gaussian
Mixture Model to this space yields a cattle identity classifier. Results show
an accuracy of Top-1 57.0% and Top-4: 76.9% and an Adjusted Rand Index: 0.53
compared to the ground truth. Whilst supervised training surpasses this
benchmark by a large margin, we conclude that self-supervision can nevertheless
play a highly effective role in speeding up labelling efforts when initially
constructing supervision information. We provide all data and full source code
alongside an analysis and evaluation of the system.
