A Trained-once Crowd Counting Method Using Differential WiFi Channel State Information

2016 ◽  
Author(s):  
Simone Di Domenico ◽  
Mauro De Sanctis ◽  
Ernestina Cianca ◽  
Giuseppe Bianchi
Keyword(s):  
Channel State Information ◽  
Counting Method ◽  
Channel State ◽  
State Information ◽  
Crowd Counting

scholarly journals Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 315
Author(s):  
Ramon F. Brena ◽  
Edgar Escudero ◽  
Cesar Vargas-Rosales ◽  
Carlos E. Galvan-Tejada ◽  
David Munoz
Keyword(s):  
Channel State Information ◽  
State Of The Art ◽  
Doppler Spectrum ◽  
Channel State ◽  
State Information ◽  
Crowd Counting ◽  
Single Link ◽  
Novel Approaches ◽  
Device Free ◽  
Link Information

Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s presence, so by identifying these distortion patterns, we can estimate the number of people in such a given space. In this work, we refine methods that leverage Channel State Information (CSI), which is used to train a classifier that estimates the number of people placed between a Wi-Fi transmitter and a receiver, and we show that the available multi-link information allows us to achieve substantially better results than state-of-the-art single link or averaging approaches, that is, those that take the average of the information of all channels instead of taking them individually. We show experimentally how the addition of each of the multiple links information helps to improve the accuracy of the prediction from 44% with one link to 99% with 6 links.

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