A Novel Device-Free Counting Method Based on Channel Status Information

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 11; p. 3981
• Main Authors: Li, Junhuai, Tu, Pengjia, Wang, Huaijun, Wang, Kan, Yu, Lei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 15.11.2018; MDPI AG
• Subjects: covariance matrix; frequency diversity; robustness; spatial diversity; wavelet domain denoising; robustness; spatial diversity; frequency diversity; wavelet domain denoising; covariance matrix
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18113981

Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread application. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed. The wavelet domain denoising is introduced to mitigate environment noise. Furthermore, the amplitude or phase covariance matrix is extracted as the eigenmatrix. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. At the same experimental environment, the accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC.