Convolutional neural network for people counting using UWB impulse radar

People counting plays a crucial role in various sensing applications such as in smart cities and shopping malls. In this paper, we propose a data-driven solution that uses a low power ultra-wideband impulse (UWB) radar to count the number of random walking people in an indoor space. A pre-processing...

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Bibliographic Details
Published in:Journal of instrumentation 2021-08, Vol.16 (8), p.P08031
Main Authors: Pham, C.-T., Luong, V.S., Nguyen, D.-K., Vu, H.H.T., Le, M.
Format: Article
Language:English
Subjects:
Artificial neural networks
Clutter
Data processing methods
Feature extraction
Instruments for environmental monitoring, food control and medical use
Neural networks
Shopping malls
Signal processing
Ultrawideband radar
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Summary:People counting plays a crucial role in various sensing applications such as in smart cities and shopping malls. In this paper, we propose a data-driven solution that uses a low power ultra-wideband impulse (UWB) radar to count the number of random walking people in an indoor space. A pre-processing signal processing method is applied to clean clutter signals from UWB radar. Instead of the conventional counting methods, which manually extract features and learned from effective data patterns, we investigated deep convolutional neural networks (CNNs) that automatically learn from the data to count the number of people in an indoor space. The CNN model could accurately predict up to 97% accuracy for up to 10 people random walking in an area of 5 × 5 m. The different settings of the CNN models, such as the data input window size, and kernel size in each layer, will be investigated.
ISSN:1748-0221
1748-0221
DOI:10.1088/1748-0221/16/08/P08031