Person Re-Identification in Identity Regression Space
This addresses scalability and adaptability issues in person re-identification for real-world deployment, offering an incremental improvement over existing methods.
The paper tackles the problems of scalability and adaptability in person re-identification by proposing an Identity Regression Space (IRS) that formulates re-id as a regression problem, achieving state-of-the-art performance on four datasets with high learning efficiency and incremental learning capability.
Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time. In this work, we present a unified solution to address both problems. Specifically, we propose to construct an Identity Regression Space (IRS) based on embedding different training person identities (classes) and formulate re-id as a regression problem solved by identity regression in the IRS. The IRS approach is characterised by a closed-form solution with high learning efficiency and an inherent incremental learning capability with human-in-the-loop. Extensive experiments on four benchmarking datasets(VIPeR, CUHK01, CUHK03 and Market-1501) show that the IRS model not only outperforms state-of-the-art re-id methods, but also is more scalable to large re-id population size by rapidly updating model and actively selecting informative samples with reduced human labelling effort.