Indoor people counting is the task of estimating the discrete number of occupants present in an enclosed space at a given time, typically framed as a multi-class classification or regression problem over a bounded range of person counts. It matters to the WiFi/CSI sensing field because accurate occupancy estimation enables passive, infrastructure-light monitoring for applications such as smart building energy management, classroom utilization, and public safety, without requiring individuals to carry or interact with any device. Key variants include small-scale fine-grained counting over narrow ranges (e.g., 1–5 or 1–7 persons), which demands high sensitivity to subtle CSI amplitude and phase changes caused by each additional body, and the related but coarser task of occupancy detection, which serves as a binary precursor or auxiliary signal to guide the more precise counting step.
Source Papers
- A Framework to Estimate Classroom Occupancy using WiFi Channel State Information ↗ — A Framework to Estimate Classroom Occupancy using WiFi Chann
- A Novel Device-Free Counting Method Based on Channel Status Information ↗ — A Novel Device-Free Counting Method Based on Channel Status
- CRPF-QC: An Efficient CSI Recurrence Plot-Based Framework for Queue Counting ↗ — CRPF-QC: An Efficient CSI Recurrence Plot-Based Framework fo
- CSI-Based People Counting in WiFi Networks: Leveraging Occupancy Detection ↗ — CSI-Based People Counting in WiFi Networks: Leveraging Occup
- MMCOUNT: Stationary Crowd Counting System Based on Commodity Millimeter-Wave Radar ↗ — MMCOUNT: Stationary Crowd Counting System Based on Commodity
- Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions ↗ — Occupancy Prediction in IoT-Enabled Smart Buildings: Technol