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RF-Driven Crowd-Size Classification via Machine Learning

2019; IEEE Antennas & Propagation Society; Volume: 18; Issue: 11 Linguagem: Inglês

10.1109/lawp.2019.2932076

1548-5757

Tarciana Cabral de Brito Guerra, Pedro M. de Sant Ana, Millena Campos, Mateus Mattos, Álvaro Augusto Machado de Medeiros, Vicente A. de Sousa,

Anomaly Detection Techniques and Applications

In this letter, we propose a machine learning solution for crowd-size classification in an indoor environment. Narrow-band radio frequency signals are used to identify a pattern according to the number of people. Experimental data collected by a low-cost software-defined radio platform are postprocessed by applying a feature mapping along with the random forest technique for classifying the crowd-size scenarios. The proposed solution has significant accuracy in classification performance.