构建多模态多平台行人重识别基准，提升复杂场景下跨模态识别能力。
Multi-modal Multi-platform Person Re-Identification: Benchmark and Method
- 设计跨模态跨平台的Uni-Prompt ReID框架，用定制提示增强特征对齐。
- 在1930个身份的MP-ReID数据集上，性能超越现有方法。
- 适合关注智能监控、无人机追踪等实际应用的研究者。
传统行人重识别（ReID）研究通常局限于静态摄像头的单一模态数据，难以应对真实场景中多模态信号日益普遍的问题。例如，城市ReID系统可能集成固定RGB摄像头、夜间红外传感器以及具备动态追踪能力的无人机。这类系统面临视角、光照和传感器模态变化带来的挑战，严重影响识别效果。为此，我们提出MP-ReID基准，一个专为多模态、多平台ReID设计的新数据集，包含1,930个身份的多源数据，涵盖RGB、红外与热成像，由无人机和地面相机在室内外环境采集。基于该基准，我们提出Uni-Prompt ReID框架，通过特定设计的提示机制，有效应对跨模态与跨平台差异。实验表明，该方法持续优于当前最优模型，为复杂动态环境下的未来研究奠定坚实基础。数据集可访问：https://mp-reid.github.io/。
原文摘要 · Abstract (English)
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/.
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