The rising need of crowd monitoring in public spaces, especially for safety purposes, pushes the research community to propose and experiment novel methods of crowd density estimation. This paper focuses on device-free RF sensing, which does not require the monitored people to carry any electronic device. In particular, the paper proposes and assesses the performance of a crowd density estimation system based on the analysis of the variations of Channel State Information (CSI) computed from unencrypted synchronization signals transmitted by a eNodeB and reflected/scattered by people located in the monitored area. The proposed method uses features extracted from the list of singular values of the CSI secant set. This approach allows to reduce the impact of CSI variations due to noise or HW instability, improving the sensitivity to CSI variations caused by human presence. The average accuracy achieved by the proposed approach is 84%, which is comparable with the accuracy achieved with WiFi based crowd density estimation systems.

De Sanctis, M., Rossi, T., Di Domenico, S., Cianca, E., Ligresti, G., & Ruggieri, M. (2019). LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD. IEEE ACCESS, 7, 159943-159951 [10.1109/ACCESS.2019.2948273].

LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD

M. De Sanctis;T. Rossi;S. Di Domenico;E. Cianca;M. Ruggieri
2019-10-18

Abstract

The rising need of crowd monitoring in public spaces, especially for safety purposes, pushes the research community to propose and experiment novel methods of crowd density estimation. This paper focuses on device-free RF sensing, which does not require the monitored people to carry any electronic device. In particular, the paper proposes and assesses the performance of a crowd density estimation system based on the analysis of the variations of Channel State Information (CSI) computed from unencrypted synchronization signals transmitted by a eNodeB and reflected/scattered by people located in the monitored area. The proposed method uses features extracted from the list of singular values of the CSI secant set. This approach allows to reduce the impact of CSI variations due to noise or HW instability, improving the sensitivity to CSI variations caused by human presence. The average accuracy achieved by the proposed approach is 84%, which is comparable with the accuracy achieved with WiFi based crowd density estimation systems.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/03 - Telecomunicazioni
eng
Con Impact Factor ISI
Crowd counting , LTE , passive device-free crowd density estimation , CSI , RF sensing , human sensing , singular value decomposition
https://ieeexplore.ieee.org/abstract/document/8876595/keywords#keywords
De Sanctis, M., Rossi, T., Di Domenico, S., Cianca, E., Ligresti, G., & Ruggieri, M. (2019). LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD. IEEE ACCESS, 7, 159943-159951 [10.1109/ACCESS.2019.2948273].
De Sanctis, M; Rossi, T; Di Domenico, S; Cianca, E; Ligresti, G; Ruggieri, M
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/225037
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