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.
Abstract (translated)
基于摄像头的动物再识别（Animal Re-ID）可以支持野生动物监测和大型户外环境中的精准畜牧业管理，这些环境中无线连接有限。在这种情况下，推理必须直接在项圈标签或围绕微控制器（MCU）构建的低功耗边缘节点上运行。然而，大多数动物再识别模型都是为工作站或服务器设计的，并且对于内存小、输入分辨率低的设备来说太大了。 为此，我们提出了一种端到端框架。首先，我们描述了最先进的Animal Re-ID模型与MCU类硬件之间的差距，表明在受到内存和输入分辨率限制的情况下，从大型教师进行直接的知识蒸馏提供的收益有限。其次，根据这一分析，我们设计了一个高精度的动物再识别架构，通过系统地缩放基于CNN的MobileNetV2骨干网以适应低分辨率输入来实现。第三，我们在一个真实世界的数据集上评估了该框架，并引入了一种数据高效微调策略，使得仅用每只动物三个新地点的新图像即可快速适应环境。 在六个公开的Animal Re-ID数据集中，我们的紧凑型模型实现了具有竞争力的检索准确率，同时将模型大小减少了两个数量级。在一个自收集的牛数据集上，部署的模型能够完全进行设备端推理，并且仅出现了较小的准确性下降，相对于集群版本来说Top-1精度没有变化。 我们证明了在MCU类设备上实现实用、可适应的动物再识别是可行的，为大规模部署到实际野外环境铺平了道路。
URL
https://arxiv.org/abs/2512.08198
