Abstract
Recent researchers have proposed using event cameras for person re-identification (ReID) due to their promising performance and better balance in terms of privacy protection, event camera-based person ReID has attracted significant attention. Currently, mainstream event-based person ReID algorithms primarily focus on fusing visible light and event stream, as well as preserving privacy. Although significant progress has been made, these methods are typically trained and evaluated on small-scale or simulated event camera datasets, making it difficult to assess their real identification performance and generalization ability. To address the issue of data scarcity, this paper introduces a large-scale RGB-event based person ReID dataset, called EvReID. The dataset contains 118,988 image pairs and covers 1200 pedestrian identities, with data collected across multiple seasons, scenes, and lighting conditions. We also evaluate 15 state-of-the-art person ReID algorithms, laying a solid foundation for future research in terms of both data and benchmarking. Based on our newly constructed dataset, this paper further proposes a pedestrian attribute-guided contrastive learning framework to enhance feature learning for person re-identification, termed TriPro-ReID. This framework not only effectively explores the visual features from both RGB frames and event streams, but also fully utilizes pedestrian attributes as mid-level semantic features. Extensive experiments on the EvReID dataset and MARS datasets fully validated the effectiveness of our proposed RGB-Event person ReID framework. The benchmark dataset and source code will be released on this https URL
Abstract (translated)
近期的研究人员提出利用事件相机进行行人重识别（ReID），因为其表现出色且在隐私保护方面更加平衡，基于事件相机的行人ReID吸引了广泛的关注。目前主流的基于事件的行人ReID算法主要集中在融合可见光和事件流数据，以及保护隐私上。虽然已取得了显著进步，但这些方法通常是在小规模或模拟的事件相机数据集上进行训练和评估的，难以对其实际识别性能和泛化能力进行全面评估。 为了解决数据稀缺的问题，本文引入了一个大规模的RGB-事件行人ReID数据集，称为EvReID。该数据集包含118,988对图像和涵盖1200名行人的身份信息，数据采集跨越了多个季节、场景及光照条件。我们还评估了15种最先进的行人ReID算法，为未来的研究在数据与基准测试方面奠定了坚实基础。 基于新构建的数据集，本文进一步提出了一种以行人属性为导向的对比学习框架TriPro-ReID，旨在增强行人重识别中的特征学习能力。该框架不仅有效地探索了RGB帧和事件流中视觉特征，还充分利用了行人的属性作为中间层次语义特征。在EvReID数据集和MARS数据集上的广泛实验充分验证了我们所提出的RGB-事件行人ReID框架的有效性。 基准数据集及源代码可在以下链接获取：[https://this-url.com](https://this-url.com)（请将URL替换为实际发布地址）。
URL
https://arxiv.org/abs/2507.13659
