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  

Publication
IEICE TRANSACTIONS on Communications   Vol.E96-B   No.10   pp.2563-2572
Publication Date: 2013/10/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E96.B.2563
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Section on Recent Progress in Antennas and Propagation in Conjunction with Main Topics of ISAP2012)
Category: Sensing
Keyword: 
human imaging,  pedestrian classification,  UWB Doppler radar,  radial velocity distribution,  k-nearest neighbor,  

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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).