论文标题
使用合成数据集的人重新识别的属性分析
Attribute analysis with synthetic dataset for person re-identification
论文作者
论文摘要
人重新识别（RE-ID）在公共安全和视频监视等应用中起着重要作用。最近，从合成数据受益的合成数据中学习的性能出色。但是，现有的合成数据集的规模很小且缺乏多样性，这阻碍了人们在现实情况下重新建立的发展。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，该引擎的显着特征是可控的。基于它，我们构建了一个大规模的合成数据集，该数据集是从不同属性（例如照明和观点）进行多样化和自定义的。其次，我们定量分析数据集属性对重新ID系统的影响。据我们所知，这是首次尝试从合成数据集的属性方面明确剖析人的重新识别。全面的实验可以帮助我们更深入地了解人的基本问题。我们的研究还为数据集构建和未来实际用法提供了有用的见解。
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.
