发布头顶视角深度相机的隐私保护行人重识别竞赛数据集与基准。
ICPR 2026 Competition on Privacy-Preserving Person Re-Identification from Top-View RGB-Depth Camera (TVRID)
- 构建四摄像头同步采集的顶视图RGB-深度数据集，含86人身份。
- 三种任务中跨模态检索最困难，但可实现模态不变学习提升性能。
- 适合研究深度感知、跨模态匹配与隐私保护的视觉算法开发者。
本文报告了ICPR 2026 TVRID竞赛，聚焦于隐私感知的顶视图行人重识别。竞赛包含由4个同步的Intel RealSense D455摄像头采集的86个身份数据，提供配对的RGB/深度流，并涵盖平面、上坡、下坡及斜视等结构化视角变化。评估设置包含三个赛道：RGB重识别、深度重识别以及RGB↔深度跨模态检索。所有提交结果在统一服务器端使用mAP和CMC-1进行排名。最终结果显示难度排序为RGB > 深度 > 跨模态，凸显了模态受限检索的挑战性，同时验证了模态不变学习的有效性。通过在Zenodo（https://zenodo.org/records/17909410）发布数据集，在GitHub（https://github.com/RaphaelDel/ICPR-TVRID）提供评估脚本与文档，TVRID建立了可复现的顶视图、基于深度的及跨模态行人重识别基准。
原文摘要 · Abstract (English)
This companion paper reports the ICPR 2026 TVRID competition on privacy-aware top-view person re-identification. We present the competition setting, the released RGB-Depth dataset, and a summary of final results with descriptions of the top entries. TVRID contains 86 identities captured by four synchronized overhead Intel RealSense D455 cameras, with paired RGB/Depth streams and structured geometric variation across flat, ascent, descent, and oblique viewpoints. The evaluation protocol includes three tracks: RGB Re-ID, Depth Re-ID, and RGB$\leftrightarrow$Depth cross-modal retrieval. Submissions are ranked using mAP and CMC-1 under a unified server-side evaluation. The final results show a clear difficulty ordering (RGB $>$ Depth $>$ Cross-Modal), highlighting both the challenge of modality-constrained retrieval and the feasibility of strong performance with modality-invariant learning. By releasing the dataset at https://zenodo.org/records/17909410, the evaluation scripts at https://github.com/RaphaelDel/ICPR-TVRID, and the accompanying documentation, TVRID establishes a reproducible benchmark for top-view, depth-based, and cross-modal person re-id.
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