1 research outputs found
Attribute analysis with synthetic dataset for person re-identification
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