ICPR 2026 Competition on Privacy-Preserving Person Re-Identification from Top-View RGB-Depth Camera (TVRID)
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Abstract (arXiv)
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 RGBleftrightarrowDepth 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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References · 10
- 2022A ConvNet for the 2020scited by 3,579
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- 2020Deep Learning for Person Re-identification: A Survey and Outlookcited by 549
- 2017Re-ranking Person Re-identification with k-reciprocal Encodingcited by 262
- 2021Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platformcited by 66
- 2017Robust Depth-based Person Re-identificationcited by 19
- 2024Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approachcited by 17
- 2025DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-IDcited by 11
- 2023Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-Identificationcited by 9
- 20212D vs. 3D LiDAR-based Person Detection on Mobile Robotscited by 6
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