Work

Animal re-identification in video through track clustering

Year: 2025

Type: article

Abstract: Monitoring a group of animals would greatly benefit from automated animal re-identification from video. Multiple Object Tracking alone does not provide a sufficiently good re-identification, hence we propose to augment the process by further clustering the output tracks. Unlike datasets for person and vehicle identification, existing animal datasets are not substantial enough to train an advanced model for conventional clustering. In this paper, we present a Classification-Based Clustering method (CBC) which employs track labels and temporal constraints to train a bespoke model for each video dataset. Our proposed method works better than using the tracks alone as animal identities. It also outperforms 13 alternative clustering methods applied to the tracking results.

Source: Pattern Analysis and Applications

DOI: 10.1007/s10044-025-01497-8

Authors: Francis J. Williams, Samuel L. Hennessey, Ludmila I. Kuncheva

Institution: Bangor University

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,390

Funder: UK Research and Innovation