Precise identification of individual cows is a fundamental prerequisite for comprehensive digital management in smart livestock farming. While existing animal identification methods excel in controlled, single-camera settings, they face severe challenges regarding cross-camera generalization. When models trained on source cameras are deployed to new monitoring nodes characterized by divergent illumination, backgrounds, viewpoints, and heterogeneous imaging properties, recognition performance often degrades dramatically. This limits the large-scale application of non-contact technologies in dynamic, real-world farming environments. To address this challenge, this study proposes a cross-camera cow identification framework based on disentangled representation learning. This framework leverages the Subspace Identifiability Guarantee (SIG) theory in the context of bovine visual recognition. By modeling the underlying physical data generation process, we designed a principle-driven feature disentanglement module that decomposes observed images into multiple orthogonal latent subspaces. This mechanism effectively isolates stable, identity-related biometric features that remain invariant across cameras, thereby substantially improving generalization to unseen cameras. We constructed a high-quality dataset spanning five distinct camera nodes, covering heterogeneous acquisition devices and complex variations in lighting and angles. Extensive experiments across seven cross-camera tasks demonstrate that the proposed method achieves an average accuracy of 86.0%, significantly outperforming the Source-only Baseline (51.9%) and the strongest cross-camera baseline method (79.8%). This work establishes a subspace-theoretic feature disentanglement framework for collaborative cross-camera cow identification, offering a new paradigm for precise animal monitoring in uncontrolled smart farming environments.

翻译：精确识别奶牛个体是智慧畜牧养殖中实现全面数字化管理的基本前提。现有动物识别方法虽然在受控的单摄像头场景中表现优异，但在跨摄像头泛化方面面临严峻挑战。当在源摄像头上训练的模型部署到具有不同光照条件、背景、视角及异构成像特性的新监控节点时，识别性能往往急剧下降，这限制了非接触式技术在动态真实养殖环境中的大规模应用。为应对这一挑战，本研究提出了一种基于解耦表征学习的跨摄像头奶牛识别框架。该框架将子空间可辨识性保证理论应用于牛只视觉识别任务中，通过对底层物理数据生成过程进行建模，设计了一个原理驱动的特征解耦模块，将观测图像分解为多个正交的潜在子空间。该机制有效分离出跨摄像头保持不变的、稳定的身份相关生物特征，从而显著提升对未见摄像头的泛化能力。我们构建了一个覆盖五个不同摄像头节点的高质量数据集，包含异构采集设备以及光照和角度的复杂变化。在七项跨摄像头任务上的大量实验表明，所提方法平均准确率达到86.0%，显著优于仅源域基线方法的51.9%以及最强的跨摄像头基线方法的79.8%。本研究建立了一个基于子空间理论的特征解耦框架，用于协同跨摄像头奶牛识别，为无约束智慧养殖环境下的精准动物监测提供了新范式。