Animal Re-Identification on Microcontrollers

• URL: http://arxiv.org/abs/2512.08198v1
• Date: Tue, 09 Dec 2025 03:09:22 GMT
• Title: Animal Re-Identification on Microcontrollers
• Abstract summary: Camera-based animal re-identification can support wildlife monitoring and precision livestock management in large outdoor environments.<n>Most Animal Re-ID models are designed for workstations or servers and are too large for devices with small memory and low-resolution inputs.<n>We propose an on-device framework, showing that straightforward knowledge distillation from large teachers offers limited benefit once memory and input resolution are constrained.
• Score: 13.780361742050436
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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