From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification
Journal:
arXiv
Published Date:
Mar 15, 2025
Abstract
Aiming to match pedestrian images captured under varying lighting conditions,
visible-infrared person re-identification (VI-ReID) has drawn intensive
research attention and achieved promising results. However, in real-world
surveillance contexts, data is distributed across multiple devices/entities,
raising privacy and ownership concerns that make existing centralized training
impractical for VI-ReID. To tackle these challenges, we propose L2RW, a
benchmark that brings VI-ReID closer to real-world applications. The rationale
of L2RW is that integrating decentralized training into VI-ReID can address
privacy concerns in scenarios with limited data-sharing regulation.
Specifically, we design protocols and corresponding algorithms for different
privacy sensitivity levels. In our new benchmark, we ensure the model training
is done in the conditions that: 1) data from each camera remains completely
isolated, or 2) different data entities (e.g., data controllers of a certain
region) can selectively share the data. In this way, we simulate scenarios with
strict privacy constraints which is closer to real-world conditions. Intensive
experiments with various server-side federated algorithms are conducted,
showing the feasibility of decentralized VI-ReID training. Notably, when
evaluated in unseen domains (i.e., new data entities), our L2RW, trained with
isolated data (privacy-preserved), achieves performance comparable to SOTAs
trained with shared data (privacy-unrestricted). We hope this work offers a
novel research entry for deploying VI-ReID that fits real-world scenarios and
can benefit the community.
