Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has achieved remarkable performance. However, in pursuit of high accuracy, researchers in the academic always focus on training with large-scale datasets at a high cost of time and label expenses, while neglect to explore the potential of performing efficient training from millions of synthetic data. To facilitate development in this field, we reviewed the previously developed synthetic dataset GPR and built an improved one (GPR+) with larger number of identities and distinguished attributes. Based on it, we quantitatively analyze the influence of dataset attribute on re-ID system. To our best knowledge, we are among the first attempts to explicitly dissect person re-ID from the aspect of attribute on synthetic dataset. This research helps us have a deeper understanding of the fundamental problems in person re-ID, which also provides useful insights for dataset building and future practical usage.

中文翻译：

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仔细研究综合：用于人员重新识别的细粒度属性分析

人员重新识别（re-ID）在公共安全和视频监控等应用中发挥着重要作用。近期，得益于合成数据引擎的普及，从合成数据中学习，取得了不俗的成绩。然而，为了追求高精度，学术界的研究人员总是专注于以高昂的时间和标签费用进行大规模数据集的训练，而忽视了从数百万合成数据中进行高效训练的潜力。为了促进这一领域的发展，我们回顾了之前开发的合成数据集 GPR，并构建了一个具有更多身份和可区分属性的改进型数据集 (GPR+)。在此基础上，我们定量分析了数据集属性对re-ID系统的影响。据我们所知，我们是从合成数据集的属性方面明确剖析人员 re-ID 的首批尝试之一。这项研究帮助我们更深入地了解人员重新识别的基本问题，这也为数据集构建和未来的实际使用提供了有用的见解。