IEICE TRANSACTIONS on Communications
Accurate and Real-Time Pedestrian Classification Based on UWB Doppler Radar Images and Their Radial Velocity Features
Summary :
The classification of human motion is an important aspect of monitoring pedestrian traffic. This requires the development of advanced surveillance and monitoring systems. Methods to achieve this have been proposed using micro-Doppler radars. However, reliable long-term data and/or complicated procedures are needed to classify motion accurately with these conventional methods because their accuracy and real-time capabilities are invariably inadequate. This paper proposes an accurate and real-time method for classifying the movements of pedestrians using ultra wide-band (UWB) Doppler radar to overcome these problems. The classification of various movements is achieved by extracting feature parameters based on UWB Doppler radar images and their radial velocity distributions. Experiments were carried out assuming six types of pedestrian movements (pedestrians swinging both arms, swinging only one arm, swinging no arms, on crutches, pushing wheelchairs, and seated in wheelchairs). We found they could be classified using the proposed feature parameters and a k-nearest neighbor algorithm. A classification accuracy of 96% was achieved with a mean calculation time of 0.55s. Moreover, the classification accuracy was 99% using our proposed method for classifying three groups of pedestrian movements (normal walkers, those on crutches, and those in wheelchairs).
- Publication
- IEICE TRANSACTIONS on Communications Vol.E96-B No.10 pp.2563-2572
- Publication Date
- 2013/10/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E96.B.2563
- Type of Manuscript
- Special Section PAPER (Special Section on Recent Progress in Antennas and Propagation in Conjunction with Main Topics of ISAP2012)
- Category
- Sensing
Authors
Kenshi SAHO
Kyoto University
Takuya SAKAMOTO
Kyoto University
Toru SATO
Kyoto University
Kenichi INOUE
Panasonic Corporation
Takeshi FUKUDA
Panasonic Corporation
Keyword
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Kenshi SAHO, Takuya SAKAMOTO, Toru SATO, Kenichi INOUE, Takeshi FUKUDA, "Accurate and Real-Time Pedestrian Classification Based on UWB Doppler Radar Images and Their Radial Velocity Features" in IEICE TRANSACTIONS on Communications,
vol. E96-B, no. 10, pp. 2563-2572, October 2013, doi: 10.1587/transcom.E96.B.2563.
Abstract: The classification of human motion is an important aspect of monitoring pedestrian traffic. This requires the development of advanced surveillance and monitoring systems. Methods to achieve this have been proposed using micro-Doppler radars. However, reliable long-term data and/or complicated procedures are needed to classify motion accurately with these conventional methods because their accuracy and real-time capabilities are invariably inadequate. This paper proposes an accurate and real-time method for classifying the movements of pedestrians using ultra wide-band (UWB) Doppler radar to overcome these problems. The classification of various movements is achieved by extracting feature parameters based on UWB Doppler radar images and their radial velocity distributions. Experiments were carried out assuming six types of pedestrian movements (pedestrians swinging both arms, swinging only one arm, swinging no arms, on crutches, pushing wheelchairs, and seated in wheelchairs). We found they could be classified using the proposed feature parameters and a k-nearest neighbor algorithm. A classification accuracy of 96% was achieved with a mean calculation time of 0.55s. Moreover, the classification accuracy was 99% using our proposed method for classifying three groups of pedestrian movements (normal walkers, those on crutches, and those in wheelchairs).
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.2563/_p
Copy
@ARTICLE{e96-b_10_2563,
author={Kenshi SAHO, Takuya SAKAMOTO, Toru SATO, Kenichi INOUE, Takeshi FUKUDA, },
journal={IEICE TRANSACTIONS on Communications},
title={Accurate and Real-Time Pedestrian Classification Based on UWB Doppler Radar Images and Their Radial Velocity Features},
year={2013},
volume={E96-B},
number={10},
pages={2563-2572},
abstract={The classification of human motion is an important aspect of monitoring pedestrian traffic. This requires the development of advanced surveillance and monitoring systems. Methods to achieve this have been proposed using micro-Doppler radars. However, reliable long-term data and/or complicated procedures are needed to classify motion accurately with these conventional methods because their accuracy and real-time capabilities are invariably inadequate. This paper proposes an accurate and real-time method for classifying the movements of pedestrians using ultra wide-band (UWB) Doppler radar to overcome these problems. The classification of various movements is achieved by extracting feature parameters based on UWB Doppler radar images and their radial velocity distributions. Experiments were carried out assuming six types of pedestrian movements (pedestrians swinging both arms, swinging only one arm, swinging no arms, on crutches, pushing wheelchairs, and seated in wheelchairs). We found they could be classified using the proposed feature parameters and a k-nearest neighbor algorithm. A classification accuracy of 96% was achieved with a mean calculation time of 0.55s. Moreover, the classification accuracy was 99% using our proposed method for classifying three groups of pedestrian movements (normal walkers, those on crutches, and those in wheelchairs).},
keywords={},
doi={10.1587/transcom.E96.B.2563},
ISSN={1745-1345},
month={October},}
Copy
TY - JOUR
TI - Accurate and Real-Time Pedestrian Classification Based on UWB Doppler Radar Images and Their Radial Velocity Features
T2 - IEICE TRANSACTIONS on Communications
SP - 2563
EP - 2572
AU - Kenshi SAHO
AU - Takuya SAKAMOTO
AU - Toru SATO
AU - Kenichi INOUE
AU - Takeshi FUKUDA
PY - 2013
DO - 10.1587/transcom.E96.B.2563
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2013
AB - The classification of human motion is an important aspect of monitoring pedestrian traffic. This requires the development of advanced surveillance and monitoring systems. Methods to achieve this have been proposed using micro-Doppler radars. However, reliable long-term data and/or complicated procedures are needed to classify motion accurately with these conventional methods because their accuracy and real-time capabilities are invariably inadequate. This paper proposes an accurate and real-time method for classifying the movements of pedestrians using ultra wide-band (UWB) Doppler radar to overcome these problems. The classification of various movements is achieved by extracting feature parameters based on UWB Doppler radar images and their radial velocity distributions. Experiments were carried out assuming six types of pedestrian movements (pedestrians swinging both arms, swinging only one arm, swinging no arms, on crutches, pushing wheelchairs, and seated in wheelchairs). We found they could be classified using the proposed feature parameters and a k-nearest neighbor algorithm. A classification accuracy of 96% was achieved with a mean calculation time of 0.55s. Moreover, the classification accuracy was 99% using our proposed method for classifying three groups of pedestrian movements (normal walkers, those on crutches, and those in wheelchairs).
ER -
English
