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http://dx.doi.org/10.6109/jkiice.2020.24.7.935

A Deep Learning Based Device-free Indoor People Counting Using CSI  

An, Hyun-seong (Department of Electronic Engineering, Chungbuk National University)
Kim, Seungku (Department of Electronic Engineering, Chungbuk National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.7, 2020 , pp. 935-941 More about this Journal
Abstract
People estimation is important to provide IoT services. Most people counting technologies use camera or sensor data. However, the conventional technologies have the disadvantages of invasion of privacy and the need to install extra infrastructure. This paper proposes a method for estimating the number of people using a Wi-Fi AP. We use channel state information of Wi-Fi and analyze that using deep learning technology. It can be achieved by pre-installed Wi-Fi infrastructure that reduce cost for people estimation and privacy infringement. The proposed algorithm uses a k-binding data for pre-processing process and a 1D-CNN learning model. Two APs were installed to analyze the estimation results of six people. The result of the accurate number estimation was 64.8%, but the result of classifying the number of people into classes showed a high result of 84.5%. This algorithm is expected to be applicable to estimate the density of people in a small space.
Keywords
IoT; CSI; Wi-Fi; People Counting; Deep Learning;
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