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.
Abstract (translated)
为了在不同光照条件下匹配行人的图像，可见光-红外线行人再识别（VI-ReID）吸引了大量的研究关注，并取得了令人鼓舞的结果。然而，在实际监控环境中，数据分布在多个设备或实体之间，这引发了隐私和所有权方面的担忧，使得现有的集中式训练方法对于VI-ReID变得不切实际。为了解决这些问题，我们提出了L2RW基准测试，旨在将VI-ReID更接近于现实应用环境。 L2RW的逻辑在于，通过将分散化训练集成到VI-ReID中可以解决数据共享监管有限场景中的隐私问题。具体而言，我们针对不同的隐私敏感度级别设计了协议和相应的算法。在我们的新基准测试中，确保模型训练满足以下条件：1）来自每个摄像头的数据完全隔离；2）不同数据实体（如特定区域的数据控制器）可以选择性地分享数据。通过这种方式，我们在严格遵守隐私约束的场景下进行了模拟，这更接近于现实情况。 我们对各种服务器端联邦学习算法进行了广泛的实验，表明分散化的VI-ReID训练是可行的。值得注意的是，在未见过的域（即新的数据实体中），我们的L2RW在使用隔离的数据进行训练时（保证隐私保护）与使用共享数据进行训练的技术最佳方法（不考虑隐私约束）相比，达到了相当的性能水平。 我们希望通过这项工作为部署适合现实场景的VI-ReID提供一个新颖的研究入口，并为社区带来益处。
URL
https://arxiv.org/abs/2503.12232
