An RGB-D camera-based indoor occupancy positioning system for complex and densely populated scenarios

Abstract

The dynamic changes in occupants’ presence, movements and other behaviours could affect the actual operation and energy consumption of buildings. The design and operation of buildings can be adjusted to be more energy efficient by thinking over the actual distribution of occupants and application scenarios. Nevertheless, accurate, nonintrusive and applicable occupancy positioning systems, especially for complex and densely populated scenarios, have not yet been established. Herein, we propose a novel indoor positioning system based on a red green blue-depth (RGB-D) camera (CIOPS-RGBD). This system utilizes multiple RGB-D cameras to capture colour and depth images from different views. A data fusion algorithm is developed according to the result from a human pose estimation method. Then, the proposed CIOPS-RGBD system was setup and verified within a multifunction room for steady and dynamic accuracy estimation. The accuracy was verified within 10 cm in most cases. Finally, the system was tested under different application scenarios with more than 25 occupants in an 86 m² space. The results demonstrate that the system can provide high-quality occupancy positioning and body orientation information for these scenarios in almost real-time, providing a solid basis to improve the actual operation and design of indoor environment creation systems.