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http://dx.doi.org/10.5515/KJKIEES.2019.30.1.28

Learning-Based People Counting System Using an IR-UWB Radar Sensor  

Choi, Jae-Ho (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
Kim, Ji-Eun (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
Kim, Kyung-Tae (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
Publication Information
The Journal of Korean Institute of Electromagnetic Engineering and Science / v.30, no.1, 2019 , pp. 28-37 More about this Journal
Abstract
In this paper, we propose a real-time system for counting people. The proposed system uses an impulse radio ultra-wideband(IR-UWB) radar to estimate the number of people in a given location. The proposed system uses learning-based classification methods to count people more accurately. In other words, a feature vector database is constructed by exploiting the pattern of reflected signals, which depends on the number of people. Subsequently, a classifier is trained using this database. When a newly received signal data is acquired, the system automatically counts people using the pre-trained classifier. We validated the effectiveness of the proposed algorithm by presenting the results of real-time estimation of the number of people changing from 0 to 10 in an indoor environment.
Keywords
IR-UWB Radar; People Counting; Feature Extraction; Classification;
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