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, have achieved remarkable performance. However, existing synthetic datasets are in small size and lack of diversity, which hinders the development of person re-ID in real-world scenarios. To address this problem, firstly, we develop a large-scale synthetic data engine, the salient characteristic of this engine is controllable. Based on it, we build a large-scale synthetic dataset, which are diversified and customized from different attributes, such as illumination and viewpoint. Secondly, we quantitatively analyze the influence of dataset attributes on re-ID system. To our best knowledge, this is the first attempt to explicitly dissect person re-ID from the aspect of attribute on synthetic dataset. Comprehensive experiments help us have a deeper understanding of the fundamental problems in person re-ID. Our research also provides useful insights for dataset building and future practical usage.

中文翻译：

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用于人员重新识别的合成数据集的属性分析

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，现有的合成数据集规模小且缺乏多样性，这阻碍了现实世界场景中行人重识别的发展。为了解决这个问题，首先，我们开发了一个大规模的合成数据引擎，这个引擎的显着特点是可控的。在此基础上，我们构建了一个大规模的合成数据集，该数据集从光照和视点等不同属性进行多样化和定制。其次，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，这是第一次尝试从合成数据集的属性方面明确剖析人员重新ID。综合实验帮助我们更深入地了解人员重识别的基本问题。我们的研究还为数据集构建和未来的实际使用提供了有用的见解。