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Deep Learning for Counting People from UWB Channel Impulse Response Signals.

Gun Lee¹, Subin An¹, Byung-Jun Jang¹

¹School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

Ultra-wideband (UWB) signals, using higher frequencies, can estimate room occupancy. Deep neural networks applied to UWB channel impulse response (CIR) data accurately classify the number of people, achieving 99% efficiency.

Keywords:

deep neural networks people counting ultra-wideband

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Area of Science:

Electrical Engineering
Computer Science
Signal Processing

Background:

Ultra-wideband (UWB) technology utilizes higher frequency bands for precise localization.
UWB signals' channel impulse response (CIR) waveform can potentially estimate the number of people in a room.

Purpose of the Study:

To apply deep neural networks (DNNs) for estimating room occupancy using UWB CIR signals.
To investigate various DNN architectures and ensemble configurations for single UWB CIR data classification.

Main Methods:

Acquisition and preprocessing of UWB CIR data.
Design and implementation of diverse DNN architectures and ensemble models.
Comparative experimental evaluation of classification performance.

Main Results:

DNNs accurately classify the number of people in a Line of Sight (LoS) environment.
Achieved 99% performance and efficiency in terms of memory size and Floating Point Operations Per Second (FLOPs).

Conclusions:

Deep neural networks are effective for non-intrusive people counting using UWB CIR data.
The proposed method offers a highly efficient solution for real-time occupancy estimation.