Learning Compact Identity Representations for Weakly Textured Hanwoo Cattle Re-Identification
TL;DR
This study proposes a visual re-identification framework that combines pose-guided image generation with viewpoint-aware feature refinement, and shows that the method improves cattle re-identification performance across different datasets and evaluation settings.
Abstract
Simple Summary The reliable identification of individual cattle is important for precision livestock farming because it supports the long-term monitoring of animal behavior, health, and welfare. However, automatically identifying Hanwoo cattle is challenging because different individuals often have very similar body appearance, and their visual features can change greatly with pose and camera viewpoint. To address this problem, this study proposes a visual re-identification framework that combines pose-guided image generation with viewpoint-aware feature refinement. Specifically, the method learns to generate pose-diverse, identity-consistent images and, during inference, aggregates their features with the original image features to obtain a more compact identity representation, which is further refined using viewpoint information. Experimental results show that the method improves cattle re-identification performance across different datasets and evaluation settings. This work provides a useful technical reference for developing non-invasive, camera-based cattle identification systems in real farm environments.