Abstract
Device-free RF sensing and monitoring of human/crowd behavior has gained great attraction and consensus. Many different systems have been proposed for different applications, e.g. crowd counting, people localization and tracking or activity recognition. This paper focuses on a relatively new application for device-free RF sensing, i.e. counting of people waiting in line. The proposed solution follows a modular approach which splits an indefinitely long and complex waiting line into chunks (modules) of small size. In each module we apply a naïve Bayes classification algorithm to statistical features of RF power measurements over links crossing the queue. We prove through experiments and combinatorial calculus that the proposed approach achieves very promising accuracy.
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