Accurate and Real-Time Pedestrian Classification Based on UWB Doppler Radar Images and Their Radial Velocity Features

Kenshi SAHO; Takuya SAKAMOTO; Toru SATO; Kenichi INOUE; Takeshi FUKUDA

doi:10.1587/transcom.E96.B.2563

Accurate and Real-Time Pedestrian Classification Based on UWB Doppler Radar Images and Their Radial Velocity Features

Kenshi SAHO, Takuya SAKAMOTO, Toru SATO, Kenichi INOUE, Takeshi FUKUDA

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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

human imaging, pedestrian classification, UWB Doppler radar, radial velocity distribution, k-nearest neighbor

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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

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@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},}

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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 -