Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data

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 integrating 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.