多模态多平台行人重识别：基准与方法
Multi-modal Multi-platform Person Re-Identification: Benchmark and Method
AI总结：
针对传统行人重识别局限于单模态静态摄像头、难以应对复杂现实场景的问题，本文构建了含1930个身份的多模态多平台MP-ReID基准，并提出适配跨场景的Uni-Prompt ReID框架，性能超越现有最优方法。
摘要：
传统行人重识别（ReID）研究通常局限于静态摄像头的单模态传感器数据，无法应对多模态信号日益普及的现实场景的复杂性。例如，考虑一个集成了固定RGB摄像头、夜间红外传感器以及配备动态跟踪能力的无人机（UAV）的城市ReID系统。这类系统因摄像头视角、光照条件和传感器模态的差异面临重大挑战，阻碍了有效的行人重识别。为应对这些挑战，我们提出了MP-ReID基准，这是一个专门为多模态多平台ReID设计的新型数据集。该基准独特地汇集了1930个身份的多模态数据，涵盖RGB、红外和热成像模态，由无人机和地面摄像头在室内外环境中采集。基于该基准，我们提出了Uni-Prompt ReID框架，它配备专门设计的提示词，适配跨模态和跨平台场景。我们的方法始终优于当前最优方法，为复杂动态ReID环境的未来研究奠定了坚实基础。我们的数据集可在以下网址获取：https://mp-reid.github.io/。
英文摘要：
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:https://mp-reid.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
