The identification of animals from video footage is
an important ecological pursuit. It presents a labour-intensive
task, requiring experts to invest considerable time analysing video
recordings. While identifying humans from video is a mainstream
research quest, much less has been done for recognising animals’
identities. This paper explores and contrasts the effectiveness
of hierarchical and centroid-based constraint clustering methods
across five manually annotated video datasets containing animals.
We aim to determine the most suitable methodology for integrat-
ing into a fully autonomous pipeline for animal re-identification.
Our experimental findings indicate that, contrary to expectation,
online hierarchical constraint clustering surpasses centroid-based
constrained clustering.