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http://dx.doi.org/10.6109/jkiice.2021.25.12.1890

WiFi CSI Data Preprocessing and Augmentation Techniques in Indoor People Counting using Deep Learning  

Kim, Yeon-Ju (Department of Electronics Engineering, Chungbuk National University)
Kim, Seungku (Department of Electronics Engineering, Chungbuk National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1890-1897 More about this Journal
Abstract
People counting is an important technology to provide application services such as smart home, smart building, smart car, etc. Due to the social distancing of COVID-19, the people counting technology attracted public attention. People counting system can be implemented in various ways such as camera, sensor, wireless, etc. according to service requirements. People counting system using WiFi AP uses WiFi CSI data that reflects multipath information. This technology is an effective solution implementing indoor with low cost. The conventional WiFi CSI-based people counting technologies have low accuracy that obstructs the high quality service. This paper proposes a deep learning people counting system based on WiFi CSI data. Data preprocessing using auto-encoder, data augmentation that transform WiFi CSI data, and a proposed deep learning model improve the accuracy of people counting. In the experimental result, the proposed approach shows 89.29% accuracy in 6 subjects.
Keywords
Data augmentation; Data preprocessing; Deep learning; People counting;
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