Privacy‑aware person re‑identification (Re‑ID) from top‑view cameras replaces frontal appearance with anthropometric shape, while depth sensing further suppresses color‑based cues. The ICPR 2026 TVRID competition used four synchronized Intel RealSense D455 units, each providing RGB and Depth streams, covering flat, ascent, descent and oblique viewpoints and capturing 86 identities. Evaluation metrics were mAP and CMC@1, computed for within‑camera in/out, ascent‑descent and flat‑oblique scenario pairs.\n\nA total of 147 participants registered and 33 final submissions were received. IIT Patna fielded three teams; “abhinavkumar03” and “sjkdgfhl” ranked 5th and 7th among 31 ranked entries, while “vivek katuri” achieved 77.67% CMC@1 and 85.20% mAP on the RGB track.\n\nMethodology 1 (abhinavkumar03) obtained 93.51% mAP and 90.42% CMC@1 on the Depth track, placing 4th out of 28 teams, a 7.3‑point gain over Methodology 2. Methodology 2 (sjkdgfhl) reached 99.63%/99.35% on RGB and 86.26%/80.34% on Depth. Both far exceeded the overall Depth average of 57.19% mAP, highlighting the importance of depth representation and supervision for privacy‑preserving Re‑ID.\n\nReview: The competition confirms that depth cues dramatically reduce reliance on color information and improve discriminative power across viewpoints. IIT Patna’s approaches demonstrate effective feature extraction and cross‑view matching strategies, offering a solid foundation for future privacy‑safe surveillance applications.