Indoor occupancy estimation is the task of determining the number of people present within a defined indoor space at a given time, using non-intrusive sensing technologies such as WiFi Channel State Information rather than cameras or dedicated hardware worn by occupants. It matters for the field because accurate occupancy counts enable applications in smart building energy management, space utilization, safety compliance, and public transport capacity planning, all without requiring personal identification or explicit user participation. Key variants include room-level counting in relatively static environments such as classrooms, as well as dynamic counting in mobile or constrained spaces such as public transport vehicles, with approaches differing in the number of transceiver pairs deployed, the scale of occupancy ranges targeted, and the methods used to fuse multi-link CSI signals into reliable estimates.
Source Papers
- A Framework to Estimate Classroom Occupancy using WiFi Channel State Information ↗ — A Framework to Estimate Classroom Occupancy using WiFi Chann
- CSI-based Passenger Counting on Public Transport Vehicles with Multiple Transceivers ↗ — CSI-based Passenger Counting on Public Transport Vehicles wi
- DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-Shot Learning ↗ — DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor
- Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT ↗ — Device-free occupancy detection and crowd counting in smart
- Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions ↗ — Occupancy Prediction in IoT-Enabled Smart Buildings: Technol
- Passive WiFi Radar for Human Sensing Using a Stand-Alone Access Point ↗ — Passive WiFi Radar for Human Sensing Using a Stand-Alone Acc
- RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-Fi Receivers ↗ — RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-