Multi-Source Information Fusion network for building occupancy estimation

Abstract

The human dimension information is crucial for efficient building energy saving, comfort conditions, health and productivity, and security management. Existing vision-based building indoor occupancy measurement approaches have achieved remarkable progress, but struggle to achieve high and robust accuracy because of the complex indoor environments. Vision-based methods face many challenges, including background objects and diverse scales, which bring practical problems to indoor applications.In this paper, to address these issues, we propose a Multi-Source Information Fusion network in video head detection for estimating building occupancy. Our method utilizes cameras to capture surveillance videos and analyzes them through a deep neural network. We use the multi-source feature to effectively guide the single-frame detector to propose robust head boxes. We apply a multi-source fusion network to extract features. Besides, we extend head detection datasets with multi-source information, including optical flow maps, Depth maps, frame difference maps, etc. Our method achieves superior performance through ablation studies compared to existing methods on practical building surveillance videos. Experiments validate its potential for building energy saving and comfort improvement with a high occupancy estimation accuracy.

Keywords: Human dimension, Building energy, Artificial intelligence, Occupancy estimation

DOI: 10.54941/ahfe1003687