A fusion framework for vision-based indoor occupancy estimation

Building occupancy information is essential for energy saving, comfort improvement, and security management. Existing vision-based indoor occupancy measurement methods have achieved remarkable progress; however, they mainly focus on single-vision situations, i.e., cameras at room entrances or interiors. These methods struggle to achieve high accuracy because of the complex indoor environments. For example, they often fail to detect occupants and generate many false positives. In this paper, to address these issues, we propose a novel fusion framework for occupancy detection and estimation based on two different perspectives. First, we design a head detection method combined with indoor scene knowledge to filter false positives and recover missed detection. Second, we propose a two-vision entrance counting method to refine the predicted results. Finally, we propose a cumulative error clearing strategy named dynamic Bayesian fusion (DBF), which integrates entrance counting and static estimation. Our framework achieves superior performance through ablation studies compared to existing methods on practical building surveillance videos, with occupancy estimation SCOREs of 99.2%, 98.5%, and 94.9%. Our framework can clear cumulative errors and stabilize estimation results. Practical experiments validate its potential for building energy saving and comfort improvement. The code is available at https://github.com/kailaisun/FFO.