arXiv:2512.08198v1 Announce Type: new 
Abstract: Camera-based animal re-identification (Animal Re-ID) can support wildlife monitoring and precision livestock management in large outdoor environments with limited wireless connectivity. In these settings, inference must run directly on collar tags or low-power edge nodes built around microcontrollers (MCUs), yet most Animal Re-ID models are designed for workstations or servers and are too large for devices with small memory and low-resolution inputs. We propose an on-device framework. First, we characterise the gap between state-of-the-art Animal Re-ID models and MCU-class hardware, showing that straightforward knowledge distillation from large teachers offers limited benefit once memory and input resolution are constrained. Second, guided by this analysis, we design a high-accuracy Animal Re-ID architecture by systematically scaling a CNN-based MobileNetV2 backbone for low-resolution inputs. Third, we evaluate the framework with a real-world dataset and introduce a data-efficient fine-tuning strategy to enable fast adaptation with just three images per animal identity at a new site. Across six public Animal Re-ID datasets, our compact model achieves competitive retrieval accuracy while reducing model size by over two orders of magnitude. On a self-collected cattle dataset, the deployed model performs fully on-device inference with only a small accuracy drop and unchanged Top-1 accuracy relative to its cluster version. We demonstrate that practical, adaptable Animal Re-ID is achievable on MCU-class devices, paving the way for scalable deployment in real field environments.

تم اقتراح إطار عمل جديد لإعادة تحديد الهوية الحيوانية المعتمد على الكاميرات (Animal Re-ID)، مما يتيح مراقبة الحياة البرية وإدارة الماشية بدقة على وحدات التحكم الدقيقة (MCUs) في بيئات ذات اتصال محدود. يتناول هذا الإطار التحديات المتعلقة بتشغيل نماذج معقدة على أجهزة منخفضة الطاقة من خلال تصميم بنية عالية الدقة تعتمد على هيكل MobileNetV2 الموسع لإدخالات منخفضة الدقة.

Se ha propuesto un nuevo marco para la re-identificación animal basada en cámaras (Animal Re-ID), que permite la monitorización de la vida silvestre y la gestión precisa del ganado en microcontroladores (MCUs) en entornos con conectividad limitada. Este marco aborda los desafíos de ejecutar modelos complejos en dispositivos de bajo consumo al diseñar una arquitectura de alta precisión basada en un backbone MobileNetV2 escalado para entradas de baja resolución.

Un nouveau cadre pour la ré-identification animale basée sur des caméras (Animal Re-ID) a été proposé, permettant la surveillance de la faune et la gestion précise du bétail sur des microcontrôleurs (MCUs) dans des environnements avec une connectivité limitée. Ce cadre répond aux défis de l'exécution de modèles complexes sur des dispositifs à faible consommation d'énergie en concevant une architecture de haute précision basée sur un backbone MobileNetV2 mis à l'échelle pour des entrées à basse résolution.

A new framework for camera-based animal re-identification (Animal Re-ID) has been proposed, enabling wildlife monitoring and precision livestock management on microcontrollers (MCUs) in environments with limited connectivity. This framework addresses the challenges of running complex models on low-power devices by designing a high-accuracy architecture based on a scaled MobileNetV2 backbone for low-resolution inputs.

Animal Re-Identification on Microcontrollers

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