Bio-inspired Motion Estimation with Event-Driven Sensors

  • Francisco BarrancoEmail author
  • Cornelia Fermuller
  • Yiannis Aloimonos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)


This paper presents a method for image motion estimation for event-based sensors. Accurate and fast image flow estimation still challenges Computer Vision. A new paradigm based on asynchronous event-based data provides an interesting alternative and has shown to provide good estimation at high contrast contours by estimating motion based on very accurate timing. However, these techniques still fail in regions of high-frequency texture. This work presents a simple method for locating those regions, and a novel phase-based method for event sensors that estimates more accurately these regions. Finally, we evaluate and compare our results with other state-of-the-art techniques.


Bio-inspired systems Neuromorphic engineering Motion estimation Event-driven sensors Asynchronous sensors 


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  1. 1.
    Adelson, E.H., Bergen, J.R.: Spatiotemporal energy models for the perception of motion. Journal of Optical Society of America, A 2(2), 284–299 (1985)CrossRefGoogle Scholar
  2. 2.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. Journal Computer Vision 92(1), 1–31 (2011)CrossRefGoogle Scholar
  3. 3.
    Barranco, F., Fermuller, C., Aloimonos, Y.: Contour motion estimation for asynchronous event-driven cameras. Proc. of the IEEE 102(10), 1537–1556 (2014)CrossRefGoogle Scholar
  4. 4.
    Barranco, F., Tomasi, M., Diaz, J., Vanegas, M., Ros, E.: Parallel architecture for hierarchical optical flow estimation based on FPGA. IEEET on VLSI 20(6), 1058–1067 (2012)CrossRefGoogle Scholar
  5. 5.
    Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. Journal Computer Vision 12, 43–77 (1994)CrossRefGoogle Scholar
  6. 6.
    Benosman, R., Clercq, C., Lagorce, X., Ieng, S.H., Bartolozzi, C.: Event-based visual flow. IEEET Neural Networks Learning Systems 25(2), 407–417 (2014)CrossRefGoogle Scholar
  7. 7.
    Benosman, R., Ieng, S.H., Clercq, C., Bartolozzi, C., Srinivasan, M.: Asynchronous frameless event-based optical flow. Neural Networks 27, 32–37 (2012)CrossRefGoogle Scholar
  8. 8.
    Berner, R., Brandli, C., Yang, M., Liu, S.C., Delbruck, T.: A 240 x 180 10mw 12us latency sparse-output vision sensor for mobile applications. In: 2013 Symposium on VLSI Circuits (VLSIC), pp. C186–C187, June 2013Google Scholar
  9. 9.
    Brandt, J.: Improved accuracy in gradient-based optical flow estimation. Int. Journal of Computer Vision 25(1), 5–22 (1997)CrossRefGoogle Scholar
  10. 10.
    Brodsky, T., Fermüller, C., Aloimonos, Y.: Structure from motion: Beyond the epipolar constraint. Int. Journal Computer Vision 37(3), 231–258 (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
    Fermüller, C.: Passive navigation as a pattern recognition problem. Int. Journal of Computer Vision 14(2), 147–158 (1995)CrossRefGoogle Scholar
  12. 12.
    Fleet, D.J., Jepson, A.D.: Computation of component image velocity from local phase information. Int. Journal of Computer Vision 5(1), 77–104 (1990)CrossRefGoogle Scholar
  13. 13.
    Heeger, D.J.: Optical flow using spatiotemporal filters. Int. Journal of Computer Vision 1(4), 279–302 (1988)CrossRefGoogle Scholar
  14. 14.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. AI vol. 17, pp. 185–203 (1981)Google Scholar
  15. 15.
    Lichtsteiner, P., Posch, C., Delbruck, T.: A 128x128 at 120db 15us latency asynchronous temporal contrast vision sensor. IEEE SSC 43(2), 566–576 (2008)Google Scholar
  16. 16.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. Conf. on Artificial Intelligence 2, 674–679 (1981)Google Scholar
  17. 17.
    Mac Aodha, O., Humayun, A., Pollefeys, M., Brostow, G.: Learning a confidence measure for optical flow. IEEET Pattern Analysis Machine Intelligence 35(5), 1107–1120 (2013)CrossRefGoogle Scholar
  18. 18.
    Mead, C.: Neuromorphic electronic systems. P. of IEEE 78(10), 1629–1636 (1990)CrossRefGoogle Scholar
  19. 19.
    Orfanidis, S.: Introduction to Signal Processing, Prentice Hall international editions. Prentice Hall (1996)Google Scholar
  20. 20.
    Otte, M., Nagel, H.H.: Optical flow estimation: Advances and comparisons. European Conference Computer Vision 800, 49–60 (1994)Google Scholar
  21. 21.
    Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: Computer Vision and Pattern Recognition, pp. 2432–2439 (2010)Google Scholar
  22. 22.
    Sun, D., Roth, S., Black, M.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. Journal of Computer Vision 106(2), 115–137 (2014)CrossRefGoogle Scholar
  23. 23.
    Tomasi, M., Barranco, F., Vanegas, M., Díaz, J., Ros, E.: Fine grain pipeline architecture for high performance phase-based optical flow computation. Journal of Systems Architecture 56(11), 577–587 (2010)CrossRefGoogle Scholar
  24. 24.
    Uras, S., Girosi, F., Verri, A., Torre, V.: A computational approach to motion perception. Biological Cybernetics 60(2), 79–87 (1988)CrossRefGoogle Scholar
  25. 25.
    Watson, A.B.: Model of human visual-motion sensing. Journal of The Optical Society of America A-optics Image Science and Vision 2 (1985)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Francisco Barranco
    • 1
    • 2
    Email author
  • Cornelia Fermuller
    • 1
  • Yiannis Aloimonos
    • 1
  1. 1.University of MarylandCollege ParkUSA
  2. 2.University of GranadaGranadaSpain

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