CSI-based crowd counting is a passive sensing technique that uses Channel State Information (CSI) extracted from Wi-Fi signals to estimate the number of people present in an indoor environment, without requiring individuals to carry any device or actively participate in the sensing process. It matters for the field because it enables scalable, privacy-preserving occupancy monitoring for applications such as building management, public safety, and emergency response, while also serving as a key benchmark task for evaluating the generalizability of Wi-Fi sensing systems across diverse real-world deployments. Key variants differ in the machine learning approach employed, ranging from traditional regression and classification models to deep learning architectures, as well as in the granularity of the count estimate and the degree to which systems can transfer across different indoor environments, which remains a central challenge given the strong environment dependence of CSI measurements.
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
- A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects ↗ — A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techni
- 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
- 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
- Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey ↗ — Deep Learning-Enhanced Human Sensing with Channel State Info
- Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT ↗ — Device-free occupancy detection and crowd counting in smart
- EasyCount: Crowd Counting Based on Easy Deployment Using Commodity Wi-Fi ↗ — EasyCount: Crowd Counting Based on Easy Deployment Using Com
- FreeCount: Device-Free Crowd Counting with Commodity WiFi ↗ — FreeCount: Device-Free Crowd Counting with Commodity WiFi
- Guiding Wi-Fi Sensor Placement for Enhanced CSI-Based Sensing in Stationary Crowd Counting ↗ — Guiding Wi-Fi Sensor Placement for Enhanced CSI-Based Sensin
- Investigation of Environment Dependence in Wi-Fi CSI-Based Crowd Counting Systems ↗ — Investigation of Environment Dependence in Wi-Fi CSI-Based C
- RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-Fi Receivers ↗ — RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-
- Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review ↗ — Sensing Technologies for Crowd Management, Adaptation, and I
- Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic ↗ — Towards Energy Efficient Wireless Sensing by Leveraging Ambi
- Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free Crowd Counting and Localization ↗ — Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free