Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time. In this work, we present a unified solution to address both problems. Specifically, we propose to construct an identity regression space (IRS) based on embedding different training person identities (classes) and formulate re-id as a regression problem solved by identity regression in the IRS. The IRS approach is characterised by a closed-form solution with high learning efficiency and an inherent incremental learning capability with human-in-the-loop. Extensive experiments on four benchmarking datasets (VIPeR, CUHK01, CUHK03 and Market-1501) show that the IRS model not only outperforms state-of-the-art re-id methods, but also is more scalable to large re-id population size by rapidly updating model and actively selecting informative samples with reduced human labelling effort.

中文翻译：

---

身份回归空间中的人员重新识别


大多数现有的人员重新识别（re-id）方法不适合现实世界的部署，原因有两个：无法扩展到大规模人群，以及随着时间的推移不适应。在这项工作中，我们提出了一个统一的解决方案来解决这两个问题。具体来说，我们建议基于嵌入不同的训练人员身份（类别）构建身份回归空间（IRS），并将re-id制定为由IRS中的身份回归解决的回归问题。 IRS 方法的特点是闭式解决方案，具有高学习效率和固有的人机循环增量学习能力。对四个基准数据集（VIPeR、CUHK01、CUHK03 和 Market-1501）的广泛实验表明，IRS 模型不仅优于最先进的重新识别方法，而且对于大型重新识别人口规模也更具可扩展性快速更新模型并主动选择信息样本，减少人工标记工作。