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Journal Article
Privacy-preserving labeling-free occupancy counting sensor based on ToF camera and clustering
- Authors
- Jaeik Jeong, Wan-Ki Park
- Issue Date
- 2025-10
- Citation
- ETRI Journal, v.47, no.5, pp.841-854
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- Occupancy detection systems are crucial for optimizing energy efficiency in smart cities and buildings but often face privacy and data dependency challenges. YOLO (you only look once), a widely used real-time detection framework, relies on identifiable image data and labeled datasets. This study proposes a privacy-preserving, labeling-free occupancy sensor using a time-of-flight (ToF) camera, and a clustering algorithm. Positioned above doorways, the ToF camera captures depth data that inherently protect privacy by avoiding identifiable information. Using the mean shift clustering algorithm, it performs real-time detection and tracking without labeled data, generating bounding boxes for movement analysis. Unlike traditional ToF-based or unsupervised methods, the proposed system adapts dynamically to varying occupant behaviors and environmental conditions for robust real-time detection. Experimental results show that the proposed method achieves over 90% accuracy in standard single-entry and exit scenarios. By addressing existing limitations, it offers a data-efficient, privacy-sensitive solution for building digital twins in energy optimization and resource management.
- KSP Keywords
- Clustering algorithm, Data Dependencies, Depth Data, Detection Framework, Detection Systems(IDS), Detection and tracking, Energy efficiency, Environmental conditions, Image data, Mean-shift(MS), Movement analysis
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
