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RF-Driven Crowd-Size Classifcation via Machine Learning
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DOI：10.1109/LAWP.2019.2932076.png)
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Abstract
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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.
Keywords:
Indoor people detection
machine learning (ML)
radio frequency (RF) signal
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