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VARe-ID

📄️ Video-Based Animal Re-Identification (VARe-ID) from Multiview Spatio-Temporal Track Clustering

This work presents a modular software pipeline and end-to-end workflow for video-based animal re-identification, which assigns consistent individual IDs by clustering multiview spatio-temporal tracks with minimal human intervention. Starting from raw video, the system detects and tracks animals, scores and selects informative left/right views, computes embeddings, clusters annotations by viewpoint, and then links clusters across time and varying perspectives using spatio-temporal continuity. Automated consistency checks resolve remaining ambiguities. Preliminary experiments demonstrate near-perfect identification accuracy with very limited manual verification. The workflow is designed to be generalizable across species. Currently, trained models support Grevy’s and Plains zebras, with plans to expand to a broader range of species.