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Object Tracking Using Multiple Neuromorphic Vision Sensors

  • Vlatko Bečanović
  • Ramin Hosseiny
  • Giacomo Indiveri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3276)

Abstract

In this paper we show how a combination of multiple neuromorphic vision sensors can achieve the same higher level visual processing tasks as carried out by a conventional vision system. We process the multiple neuromorphic sensory signals with a standard auto-regression method in order to fuse the sensory signals and to achieve higher level vision processing tasks at a very high update rate. We also argue why this result is of great relevance for the application domain of reactive and lightweight mobile robotics, at the hands of a soccer robot, where the fastest sensory-motor feedback loop is imperative for a successful participation in a RoboCup soccer competition.

Keywords

Neuromorphic vision sensors analog VLSI reactive robot control sensor fusion RoboCup 

References

  1. 1.
  2. 2.
    Bruce, J., Balch, T., Veloso, M.: Fast and inexpensive color image segmentation for interactive robots. In: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), vol. 3, pp. 2061–2066 (2000)Google Scholar
  3. 3.
  4. 4.
    Kubina, S.: Konzeption, Entwicklung und Realisierung – Micro-Controller basierter Schnittstellen für mobile Roboter. Diploma thesis at GMD Schloss Birlinghoven (2001) (in German)Google Scholar
  5. 5.
    Indiveri, G.: Neuromorphic Analog VLSI Sensor for Visual Tracking: Circuits and Application Examples. IEEE Transactions on Circuits and Systems II, Analog and Digital Signal Processing 46(11), 1337–1347 (1999)CrossRefGoogle Scholar
  6. 6.
    Kramer, J., Sarpeshkar, R., Koch, C.: Pulse-based analog VLSI velocity sensors. IEEE Transactions on Circuits and Systems II, Analog and Digital Signal Processing 44(2), 86–101 (1997)CrossRefGoogle Scholar
  7. 7.
    Stocker, A., Douglas, R.J.: Computation of Smooth Optical Flow in a Feedback Connected Analog Network. In: Kearns, M.S., Solla, S.A., Cohn, D. (eds.) Advances in Neural Information Processing Systems, vol. 11. MIT Press, Cambridge (1999)Google Scholar
  8. 8.
    Bredenfeld, A., Kobialka, H.-U.: Team Cooperation Using Dual Dynamics. In: Hannebauer, M., Wendler, J., Pagello, E. (eds.) ECAI-WS 2000. LNCS (LNAI), vol. 2103, pp. 111–124. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Bredenfeld, A., Indiveri, G.: Robot Behavior Engineering using DD-Designer. In: Proc. IEEE/RAS International Conference on Robotics and Automation, ICRA (2001)Google Scholar
  10. 10.
    Hosseiny, R.: Fusion of Neuromorphic Vision Sensors for a mobile robot, Master thesis RWTH Aachen (2003)Google Scholar
  11. 11.
    Ljung, L.: System Identification: Theory for the User. Prentice Hall, Englewood Cliffs (1999)Google Scholar
  12. 12.
    Brooks, R.: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation RA-2(1) (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Vlatko Bečanović
    • 1
  • Ramin Hosseiny
    • 1
  • Giacomo Indiveri
    • 1
  1. 1.Fraunhofer Institute of Autonomous Intelligent SystemsSankt AugustinGermany

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