Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation process, leading to poor scalability in practical re-id applications. On the other hand unsupervised re-id methods do not need identity label information, but they usually suffer from much inferior and insufficient model performance. To overcome these fundamental limitations, we propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process, significantly reducing the amount of human annotation efforts. Consequently, it gives rise to a more scalable and more feasible setting, which we call Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for self-discovering the cross-camera identity correspondence in a per-camera multi-task inference framework. Extensive experiments demonstrate the cost-effectiveness superiority of our method over the alternative approaches on three large person re-id datasets. For example, MATE yields 88.7% rank-1 score on Market-1501 in the proposed ICS person re-id setting, significantly outperforming unsupervised learning models and closely approaching conventional fully supervised learning competitors.

现有的人员重新识别（re-id）方法主要利用一大套跨摄像机身份标记的训练数据。这需要繁琐的数据收集和注释过程，从而导致实际re-id应用程序中的可伸缩性较差。另一方面，无监督的re-id方法不需要身份标签信息，但是它们通常会遭受劣等和不足的模型性能。为了克服这些基本局限性，我们提出了一种基于独立的每台摄像机身份注释的新颖的人员重新身份识别范式。这消除了最耗时且乏味的摄像机间身份标记过程，从而大大减少了人工注释的工作量。因此，它带来了更可扩展，更可行的设置，我们称之为相机内监督（ICS）人员身份，为此我们制定了多任务多重（MATE）深度学习方法。具体来说，MATE设计用于在每个摄像机的多任务推理框架中自发现跨摄像机的身份对应关系。大量实验表明，在三个大人物re-id数据集上，我们的方法在替代方法上具有成本效益优势。例如，在拟议的ICS人员重新设置中，MATE在Market-1501上获得88.7％的1级得分，显着优于无监督学习模型，并且与传统的完全受监督学习竞争者非常接近。