Counting via LED sensing : inferring occupancy using lighting infrastructure

• Main Authors: Yang, Yanbing, Luo, Jun, Hao, Jie, Pan, Sinno Jialin
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Occupancy Inference; Engineering::Computer science and engineering; Visible Light Sensing
• Online Access: https://hdl.handle.net/10356/86175; http://hdl.handle.net/10220/49263
• Institution: Nanyang Technological University

As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attention for nearly two decades. Nevertheless, existing solutions mostly rely on either pre-deployed infrastructures or user device participation, thus hampering their wide adoption. This paper presents CeilingSee, a dedicated occupancy inference system free of heavy infrastructure deployments and user involvements. Building upon existing LED lighting systems, CeilingSee converts part of the ceiling-mounted LED luminaires to act as sensors, sensing the variances in diffuse reflection caused by occupants. In realizing CeilingSee, we first re-design the LED driver to leverage LED’s photoelectric effect so as to transform a light emitter to a light sensor. In order to produce accurate occupancy inference, we then engineer efficient learning algorithms to fuse sensing information gathered by multiple LED luminaires. We build a testbed covering a 30 m2 office area; extensive experiments show that CeilingSee is able to achieve very high accuracy in occupancy inference.