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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Journal of Instrumentation
2021
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Online Access: | https://iopscience.iop.org/article/10.1088/1748-0221/16/08/P08031 https://dlib.phenikaa-uni.edu.vn/handle/PNK/2860 |
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oai:localhost:PNK-28602022-08-17T05:54:48Z Convolutional neural network for people counting using UWB impulse radar C.-T. Pham V.S. Luong D.-K. Nguyen H.H.T. Vu M. Le1 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 2021-09-14T07:14:55Z 2021-09-14T07:14:55Z 2021 Bài trích https://iopscience.iop.org/article/10.1088/1748-0221/16/08/P08031 https://dlib.phenikaa-uni.edu.vn/handle/PNK/2860 eng Journal of Instrumentation |
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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 |
format |
Bài trích |
author |
C.-T. Pham V.S. Luong D.-K. Nguyen H.H.T. Vu M. Le1 |
spellingShingle |
C.-T. Pham V.S. Luong D.-K. Nguyen H.H.T. Vu M. Le1 Convolutional neural network for people counting using UWB impulse radar |
author_facet |
C.-T. Pham V.S. Luong D.-K. Nguyen H.H.T. Vu M. Le1 |
author_sort |
C.-T. Pham |
title |
Convolutional neural network for people counting using UWB impulse radar |
title_short |
Convolutional neural network for people counting using UWB impulse radar |
title_full |
Convolutional neural network for people counting using UWB impulse radar |
title_fullStr |
Convolutional neural network for people counting using UWB impulse radar |
title_full_unstemmed |
Convolutional neural network for people counting using UWB impulse radar |
title_sort |
convolutional neural network for people counting using uwb impulse radar |
publisher |
Journal of Instrumentation |
publishDate |
2021 |
url |
https://iopscience.iop.org/article/10.1088/1748-0221/16/08/P08031 https://dlib.phenikaa-uni.edu.vn/handle/PNK/2860 |
_version_ |
1751856266569842688 |
score |
8.891145 |