Attribute analysis with synthetic dataset for person re-identification
This work addresses the challenge of improving person re-identification for public security and video surveillance applications by enhancing synthetic data diversity, though it is incremental as it builds on existing synthetic data approaches.
The paper tackles the problem of limited diversity in synthetic datasets for person re-identification by developing a controllable synthetic data engine to create a large-scale, diversified dataset, and quantitatively analyzes the influence of attributes like illumination and viewpoint on re-ID performance, with experiments providing deeper insights into fundamental issues.
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.