IIT Patna 团队参加 ICPR 2026 TVRID 顶视角 RGB-D 隐私保护行人重识别竞赛方法报告

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Abstract:Privacy-aware person re-identification (Re-ID) from top-view cameras replaces frontal appearance with anthropometric shape, while depth sensing further reduces color-based identity cues. The ICPR 2026 TVRID competition evaluates this setting using 86 identities captured by four synchronized Intel RealSense D455 cameras with RGB and Depth streams across flat, ascent, descent, and oblique viewpoints. Performance is measured using mAP and CMC@1 across within-camera in/out, ascent-descent, and flat-oblique scenarios. The competition had 147 registered participants and 33 final submissions. Three teams from IIT Patna participated: 'abhinavkumar03' and 'sjkdgfhl_' ranked 5th and 7th among 31 ranked participants, and 'vivek katuri' reached 77.67% CMC@1 / 85.20% mAP on the RGB track. This paper presents the methods of the two top-10 teams. Methodology 1 (abhinavkumar03) achieves 93.51% mAP and 90.42% CMC@1 on Depth, ranking 4th of 28, while Methodology 2 (sjkdgfhl_) reaches 99.63%/99.35% on RGB and 86.26%/80.34% on Depth. Both substantially outperform the 57.19% track-average Depth mAP, with Methodology 1 yielding a 7.3-point gain over Methodology 2. These results demonstrate the importance of depth representation and supervision for privacy-preserving Re-ID.

Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2611.12960 [cs.AI]
	(or arXiv:2611.12960v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2611.12960 arXiv-issued DOI via DataCite

Submission history

From: Bibek Das [view email]
[v1] Sat, 7 Nov 2026 09:22:42 UTC (94 KB)