标题： 当行人重识别遇到事件相机：一个基准数据集和一个属性引导的行人重识别框架 显示英文标题

标题： When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework

摘要： 最近的研究人员由于其出色的性能和在隐私保护方面的更好平衡，提出了使用事件相机进行人员重识别（ReID），基于事件相机的人员ReID引起了广泛关注。 目前，主流的基于事件的人员ReID算法主要专注于融合可见光和事件流，以及保护隐私。 尽管取得了显著进展，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，使得难以评估其真实的识别性能和泛化能力。 为了解决数据稀缺的问题，本文引入了一个大规模的RGB-事件基于人员ReID数据集，称为EvReID。 该数据集包含118,988对图像，并涵盖了1200个行人身份，数据收集覆盖了多个季节、场景和光照条件。 我们还评估了15种最先进的人员ReID算法，为未来研究在数据和基准测试方面奠定了坚实的基础。 基于我们新构建的数据集，本文进一步提出了一种行人属性引导的对比学习框架，以增强人员重识别的特征学习，称为TriPro-ReID。 该框架不仅能够有效探索来自RGB帧和事件流的视觉特征，而且还能充分利用行人属性作为中级语义特征。 在EvReID数据集和MARS数据集上的大量实验充分验证了我们提出的RGB-事件人员ReID框架的有效性。 基准数据集和源代码将发布在https://github.com/Event-AHU/Neuromorphic_ReID 显示英文摘要

摘要： 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 https://github.com/Event-AHU/Neuromorphic_ReID