CSI-based people counting is a passive sensing technique that uses Channel State Information extracted from Wi-Fi signals to estimate the number of individuals present in an environment by analyzing how human bodies perturb radio frequency propagation. It matters to the field because it enables non-intrusive, privacy-preserving occupancy monitoring without requiring subjects to carry dedicated devices, with practical applications in smart building management, energy efficiency, and security. Key variants include approaches that differ in input representation — such as raw CSI amplitude time series, preprocessed colormap-transformed inputs like RGB-encoded amplitude matrices, and multi-subcarrier spectral features — as well as in the underlying models used, ranging from classical machine learning classifiers to deep convolutional neural networks trained on labeled CSI datasets.
Source Papers
- A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels ↗ — A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Cha
- 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
- Channel State Information (CSI) Amplitude Coloring Scheme for Enhancing Accuracy of an Indoor Occupancy Detection System Using Wi-Fi Sensing ↗ — Channel State Information (CSI) Amplitude Coloring Scheme fo