Human-Centric Depth Estimation: A Hybrid Approach with Minimal Data

• Published in: Electronics (Basel) Vol. 14; no. 11; p. 2283
• Main Authors: Kim, Yuhyun, Ahn, Heejin, Kim, Taeseop, Ahn, Byungtae, Choi, Dong-Geol
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 04.06.2025
• Subjects: Accuracy; Cameras; Closed circuit television; Datasets; Evacuations & rescues; Human subjects; Paradigms; Route optimization; Surveillance
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics14112283

This study presents a novel system for accurate camera-to-person distance estimation in CCTV environments. To address the limitations of existing approaches—which often require extensive training data and lack object-level precision—we propose a hybrid framework that integrates SAM’s zero-shot segmentation with monocular depth estimation. Our method isolates human subjects from complex backgrounds and incorporates Kernel Density Estimation (KDE), log-space learning, and linear residual blocks to improve prediction accuracy. This approach is designed to resolve the non-linear mapping between visual features and metric distances. Evaluations on a custom dataset demonstrate a mean absolute error (MAE) of 0.65 m on 1612 test images, using only 30 training samples. Notably, the use of SAM for fine-grained segmentation significantly outperforms conventional bounding box methods, reducing the MAE from 0.82 m to 0.65 m. The proposed system offers immediate applicability to security surveillance and disaster response scenarios, with its minimal data requirements enhancing its practical deployability.