Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Feature-based human motion parameter estimation with radar

Feature-based human motion parameter estimation with radar

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Radar, Sonar & Navigation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Radar can be an extremely useful sensing technique to observe persons. It perceives persons behind walls or at great distances and in situations where persons have no or poor visibility. Human motion modulates the radar signal which can be observed in the spectrogram of the received signal. Extraction of these movements enables the animation of a person in virtual reality. The authors focus on a fast feature-based approach to estimate human motion features for real-time applications. The human walking model of Boulic is used, which describe the human motion with three parameters. Personification information is obtained by estimating the individual leg and torso parameters. These motion parameters can be estimated from the temporal maximum, minimum and centre velocity of the human motion distribution. Three methods are presented to extract these velocities. Additionally, we extract an independent human motion repetition frequency estimate based on velocity slices in the spectrogram. Kalman filters smooth the parameters and estimate the global Boulic parameters. These estimated parameters are input to the human model of Boulic which forms the basis for animation. The methods are applied to real radar measurements. The animated person generated with the extracted parameters provides a realistic look-alike of the real motion of the person.

References

    1. 1)
    2. 2)
      • A.K. Mitra , M. Kobold , T. Lewis . Theoretical radar-Doppler models for pivoting mechanical and biological objects-of-interest. Proc. SPIE – Algorithms Synth. Aperature Radar Imagery XIII , 25 - 32
    3. 3)
      • I.T. Young , J.J. Gerbrands , L.J. van Vliet . (1998) Fundamentals of image processing.
    4. 4)
      • R. Boulic , B. Ulicny , D. Thalmann . Versatile walk engine. J of Game Develop. , 1 , 29 - 54
    5. 5)
      • S. Blackman , R. Popoli . (1999) Design and analysis of modern tracking systems.
    6. 6)
      • J.L. Geisheimer , E.F. Greneker , W.S. Marshall . A high-resolution Doppler model of human gait. Proc. SPIE – Radar Sensor Technol. Data Vis. , 8 - 18
    7. 7)
      • M. Hewish . The last line of defense, reducing the vulnerability of fixed installations. Janes's Int. Def. Rev. , 28 - 35
    8. 8)
      • Thayaparan, T., Abrol, S., Riseborough, E.: `Micro-doppler radar signatures for intelligent target recognition', Technical Report DRDC Ottawa TM 2004-170, September 2004.
    9. 9)
      • Nalecz, M., Rytel-Andrianik, R., Wojtkiewicz, A.: `Micro-Doppler analysis of signals received by FMCW radar', Int. Radar Symp., 2003, p. 651–656.
    10. 10)
      • V.G. Nebabin . (1995) Methods and techniques of radar recognition.
    11. 11)
      • M.I. Skolnik . (2001) Introduction to radar systems.
    12. 12)
      • P. Withington , H. Fluhler , S. Nag . Enhancing homeland security with advanced UWB sensors. IEEE Microw. Mag. , 5 , 51 - 58
    13. 13)
      • Ballreich, C.: ‘Vrml nancy’, Humanoid Animation Working Group, available at: http://www.h-anim.org, 1997.
    14. 14)
    15. 15)
      • Ling, H.: `Microdoppler exploitation: preliminary data collection and analysis', NICOP Meeting, 7 June 2004, London.
    16. 16)
      • Rohling, H., Folster, F., Kruse, F.: `Target classification based on a GHz radar network', Radar 2004, Int. Conf. on Radar Systems, 2004.
    17. 17)
    18. 18)
      • Woltkiewicz, A., Nalecz, M., Kulpa, W.: `Use of polynomial phase modeling to fmcw radar. Part c: estimation of target accelerationin fmcw radars', NATO Research and Technology Agency, Sensors and Electronics Technology Symp. Passive and LPI Radio Frequency Sensors, 23–25 April 2001, paper number 40C.
    19. 19)
      • Marple, L.: `Sharping techniques for sensor feature enhancement', 26 May 2005.
    20. 20)
      • C.Y. Yam , M.S. Nixon , J.N. Carter . Automated person recognition by walking and running via model-based approaches. J. Pattern Recognit. Soc. , 5 , 1057 - 1072
    21. 21)
    22. 22)
      • van Dorp, P., Groen, F.C.A.: `Real-time human walking estimation with radar', Proc. Int. Radar Symp. (IRS) 2003, 2003, Dresden, Germany.
    23. 23)
      • M.I. Skolnik . (1990) Radar handbook.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn_20070086
Loading

Related content

content/journals/10.1049/iet-rsn_20070086
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address