Person Re-Identification (ReID) across non-overlapping cameras is a challenging task, and most works in prior art rely on supervised feature learning from a labeled dataset to match the same person in different views. However, it demands the time-consuming task of labeling the acquired data, prohibiting its fast deployment in forensic scenarios. Unsupervised Domain Adaptation (UDA) emerges as a promising alternative, as it performs feature adaptation from a model trained on a source to a target domain without identity-label annotation. However, most UDA-based methods rely upon a complex loss function with several hyper-parameters, hindering the generalization to different scenarios. Moreover, as UDA depends on the translation between domains, it is crucial to select the most reliable data from the unseen domain, avoiding error propagation caused by noisy examples on the target data — an often overlooked problem. In this sense, we propose a novel UDA-based ReID method that optimizes a simple loss function with only one hyper-parameter and takes advantage of triplets of samples created by a new offline strategy based on the diversity of cameras within a cluster. This new strategy adapts and regularizes the model, avoiding overfitting the target domain. We also introduce a new self-ensembling approach, which aggregates weights from different iterations to create a final model, combining knowledge from distinct moments of the adaptation. For evaluation, we consider three well-known deep learning architectures and combine them for the final decision. The proposed method does not use person re-ranking nor any identity label on the target domain and outperforms state-of-the-art techniques, with a much simpler setup, on the Market to Duke, the challenging Market1501 to MSMT17, and Duke to MSMT17 adaptation scenarios.

中文翻译：

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用于跨域行人重识别的无监督自适应技术

跨非重叠相机的行人重新识别 (ReID) 是一项具有挑战性的任务，现有技术中的大多数工作都依赖于从标记数据集中进行的监督特征学习，以匹配不同视图中的同一个人。然而，它需要对获取的数据进行标记的耗时任务，从而阻碍了其在取证场景中的快速部署。无监督域适应 (UDA) 作为一种有前途的替代方案出现，因为它执行从在源上训练的模型到没有身份标签注释的目标域的特征适应。然而，大多数基于 UDA 的方法依赖于具有多个超参数的复杂损失函数，阻碍了对不同场景的泛化。此外，由于 UDA 依赖于域之间的转换，因此从看不见的域中选择最可靠的数据至关重要，避免由目标数据上的噪声示例引起的错误传播——一个经常被忽视的问题。从这个意义上说，我们提出了一种新的基于 UDA 的 ReID 方法，该方法仅使用一个超参数优化简单的损失函数，并利用基于集群内相机多样性的新离线策略创建的三元组样本。这种新策略调整和规范模型，避免过拟合目标域。我们还引入了一种新的自集成方法，它聚合来自不同迭代的权重以创建最终模型，结合来自不同适应时刻的知识。为了进行评估，我们考虑了三种著名的深度学习架构，并将它们结合起来做出最终决定。