TL;DR
This paper introduces the ICPR 2026 TVRID competition, providing a new dataset and benchmark for privacy-aware top-view person re-identification using RGB and Depth data, with results highlighting modality challenges.
Contribution
It presents a novel dataset, evaluation protocol, and baseline results for privacy-preserving top-view person re-identification across multiple modalities.
Findings
RGB modality outperforms Depth in re-identification accuracy.
Cross-modal retrieval remains more challenging than single-modality tasks.
Strong modality-invariant learning methods can improve cross-modal re-identification.
Abstract
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 RGBDepth 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.…
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