Abstract
Conventional person re-identification (ReID) research is often limited to single-modality sensor data from static cameras, which fails to address the complexities of real-world scenarios where multi-modal signals are increasingly prevalent. For instance, consider an urban ReID system integrating stationary RGB cameras, nighttime infrared sensors, and UAVs equipped with dynamic tracking capabilities. Such systems face significant challenges due to variations in camera perspectives, lighting conditions, and sensor modalities, hindering effective person ReID. To address these challenges, we introduce the MP-ReID benchmark, a novel dataset designed specifically for multi-modality and multi-platform ReID. This benchmark uniquely compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging, captured by both UAVs and ground-based cameras in indoor and outdoor environments. Building on this benchmark, we introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios. Our method consistently outperforms state-of-the-art approaches, establishing a robust foundation for future research in complex and dynamic ReID environments. Our dataset are available at:this https URL.
Abstract (translated)
传统的人员再识别（ReID）研究通常局限于静态摄像头采集的单一模态传感器数据，这无法应对现实场景中的复杂性问题，这些场景中多模态信号越来越普遍。例如，在一个集成固定RGB相机、夜间红外传感器和装有动态跟踪能力无人机的城市ReID系统中，由于视角变化、光照条件以及传感器模式的不同，这类系统面临着重大挑战，阻碍了有效的人体再识别。为了解决这些问题，我们引入了MP-ReID基准测试，这是一个专门针对多模态和跨平台人员再识别的新数据集。该基准测试从1,930个不同身份中收集了多样化的模态数据，包括RGB、红外和热成像图像，并且这些数据由无人机和地面相机在室内和室外环境中捕获。 基于这一基准，我们提出了Uni-Prompt ReID框架，这是一个设计有特定提示的系统，专为跨模态和多平台场景而量身定制。我们的方法在各种测试中始终超越现有最先进技术，奠定了未来复杂且动态环境下的ReID研究的基础。 数据集可以在以下网址获取：[此链接](this%20https%20URL)
URL
https://arxiv.org/abs/2503.17096
