Skip to main content

A Robust Analog VLSI Reichardt Motion Sensor

Abstract

Silicon imagers with integrated motion-detection circuitry have been developed and tested for the past 15 years. Many previous circuits estimate motion by identifying and tracking spatial or temporal features. These approaches are prone to failure at low SNR conditions, where feature detection becomes unreliable. An alternate approach to motion detection is an intensity-based spatiotemporal correlation algorithm, such as the one proposed by Hassenstein and Reichardt in 1956 to explain aspects of insect vision. We implemented a Reichardt motion sensor with integrated photodetectors in a standard CMOS process. Our circuit operates at sub-microwatt power levels, the lowest reported for any motion sensor. We measure the effects of device mismatch on these parallel, analog circuits to show they are suitable for constructing 2-D VLSI arrays. Traditional correlation-based sensors suffer from strong contrast dependence. We introduce a circuit architecture that lessens this dependence. We also demonstrate robust performance of our sensor to complex stimuli in the presence of spatial and temporal noise.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    C. Koch and G. LaurentComplexity and the Nervous System.'' Science 284, pp.96-98,1999.

    Google Scholar 

  2. 2.

    C. Mead, Analog VLSI and Neural Systems. Reading, MA, Addison-Wesley,1989.

    Google Scholar 

  3. 3.

    R. Etienne-Cummings, J. Van der Spiegel, and P. MuellerA focal plane visual motion measurement sensor.'' IEEE Transactions on Circuits and Systems I 44, pp.55-66,1997.

    Google Scholar 

  4. 4.

    G. L. BarrowsFeature tracking linear optic Øow sensor for 2-D optic Øow measurement,'' in Proceedings of the International Conference on Automation, Robotics, and Computer Vision (ICARCV'98), December1998.

  5. 5.

    J. KramerCompact integrated motion sensor with three-pixel interaction.'' IEEE Transactions on Pattern Analysis and Machine Intelligence 18, pp.455-460,1996.

    Google Scholar 

  6. 6.

    R. Sarpeshkar, J. Kramer, G. Indiveri, and C. KochAnalog VLSI architectures for motion processing: from fundamental limits to system applications.'' Proceedings of the IEEE 84, pp.969-987,1996.

    Google Scholar 

  7. 7.

    J. Kramer, R. Sarpeshkar, and C. KochPulse-based analog VLSI velocity sensors.'' IEEE Transactions on Circuits and Systems II 44, pp. 86-101,1997.

    Google Scholar 

  8. 8.

    C. M. Higgins, R. A. Deutschmann, and C. KochPulse-based 2D motion sensors,'' in IEEE Transactions on Circuits and Systems II, to appear.

  9. 9.

    C. M. Higgins, personal correspondence, May 20,1999.

  10. 10.

    J. Tanner and C. MeadAn integrated analog optical motion sensor,'' in VLSI Signal Processing II, New York, IEEE Press, pp.59-76,1986.

    Google Scholar 

  11. 11.

    R. A. Deutschmann and C. KochCompact real-time 2-D gradient based analog VLSI motion sensor,'' in Proceedings of Fig. 12. Robustness with spatial noise. Performance of Reichardt detector array on direction discrimination with a 1/f pattern moving at a constant velocity of 14_/s while 1/f spatial noise was added. Error bars show one standard deviation of the time response, and represent residual deterministic pattern dependence such as that seen in Fig. 4.

  12. 12.

    E. H. Adelson and J. R. BergenSpatiotemporal energy models for the perception of motion.'' Journal of the Optical Society of America A 2, pp.284-299,1985.

    Google Scholar 

  13. 13.

    B. Hassenstein and W. ReichardtSystemtheoretische Analyse der Zeit-, Reihenfolgen-, und Vorzeichenauswertung bei der Bewegungsperzeption des Ru»sselka»fers Chlorophanus.'' Z. Naturforch. 11b, pp.513-524,1956.

  14. 14.

    A. Borst and M. EgelhaafPrinciples of visual motion detection.'' Trends in Neuroscience 12, pp.297-306,1989.

    Google Scholar 

  15. 15.

    H. B. Barlow and W. R. LevickThe mechanism of directionally selective units in the rabbit's retina.'' Journal of Physiology 178, pp.447-504,1965.

    Google Scholar 

  16. 16.

    T. Horiuchi, J. Lazzaro, A. Moore, and C. KochA delay-line based motion detection chip,'' in Advances in Neural Information Processing Systems 3, R. Lippman, et al., Eds., San Mateo, CA, Morgan Kaufman, pp.406-412,1991.

    Google Scholar 

  17. 17.

    R. G. Benson and T. Delbru»ckDirection selective silicon retina that uses null inhibition,'' in Advances in Neural Information Processing Systems 4, R. Lippman, J. Moody, and D. S. Touretzky. Eds., San Mateo, CA, Morgan Kaufman, 1992.

    Google Scholar 

  18. 18.

    T. Delbru»ckSilicon retina with correlation-based velocitytuned pixels.'' IEEE Transactions on Neural Networks 4, pp.529-541,1993.

  19. 19.

    A. G. Andreou, K. Strohbehn, and R. E. JenkinsSilicon retina for motion computation,'' in Proceedings of the 1991 IEEE International Symposium on Circuits and Systems, pp. 1373-1376, Singapore, June1991.

  20. 20.

    R. R. Harrison and C. KochAn analog VLSI model of the Øy elementary motion detector'' in Advances in Neural Information Processing Systems. M. I. Jordan, M. J. Kearns, and S. A. Solla. Eds., Cambridge, MA: MIT Press, 10, pp. 880-886,1998.

    Google Scholar 

  21. 21.

    A. Moini, A. Bouzerdoum, K. Eshragian, A. Yakovleff, X. T. Nguyen, A. Blanksby, R. Beare, D. Abbott, and R. E. BognerAn insect vision-based motion detection chip.'' IEEE Journal of Solid State Circuits 32, pp.279-284,1997.

    Google Scholar 

  22. 22.

    H.-C. Jiang and C.-Y. WuA 2-D velocity-and directionselective sensor with BJT-based silicon retina and temporal zero-crossing detector.'' IEEE Journal of Solid State Circuits 34, pp.241-247,1999.

    Google Scholar 

  23. 23.

    J.-M. Pichon, C. Blanes, and N. FranceschiniVisual guidance of a mobile robot equipped with a network of selfmotion sensors,'' in Proceedings of SPIE 1195, pp.44-53, 1989.

  24. 24.

    N. Franceschini, J. M. Pichon, and C. BlanesFrom insect vision to robot vision.'' Philisophical Transactions of the Royal Society London B 337, pp.283-294,1992.

    Google Scholar 

  25. 25.

    J. P. H. van Santen and G. SperlingElaborated Reichardt detectors.'' Journal of the Optical Society of America A 2, pp.300-321,1985.

  26. 26.

    A. B. Watson and A. J. AhumadaModel of human visualmotion sensing.'' Journal of the Optical Society of America A 2, pp.322-342,1985.

    Google Scholar 

  27. 27.

    A. Borst and M. EgelhaafDirection selectivity of blowØy motion-sensitive neurons is computed in a two-stage process.'' Proceedings of the National Academy of Sciences USA 87, pp.9363-9367,1990.

    Google Scholar 

  28. 28.

    M. Egelhaaf, A. Borst, and W. ReichardtComputational structure of a biological motion-detection system as revealed by local detector analysis in the Øy's nervous system.'' Journal of the Optical Society of America A 6, pp.1070-1087, 1989.

    Google Scholar 

  29. 29.

    W. Reichardt and M. EgelhaafProperties of individual movement detectors as derived from behavioural experiments on the visual system of the Øy.'' Biological Cybernetics 58, pp.287-294,1988.

    Google Scholar 

  30. 30.

    S. Single and A. BorstDendritic integration and its role in computing image velocity.'' Science 281, pp.1848-1850, 1998.

    Google Scholar 

  31. 31.

    T. Delbru»ck and C. A. MeadAnalog VLSI phototransduction by continuous-time, adaptive, logarithmic photoreceptor circuits.'' CNS Memo 30, California Institute of Technology, 1996.

  32. 32.

    M. Egelhaaf and A. BorstTransient and steady-state response properties of movement detectors.'' Journal of the Optical Society of America A 6, pp.116-127,1989.

    Google Scholar 

  33. 33.

    R. R. Harrison, P. Hasler, and B. A. MinchFloating-gate CMOS analog memory cell array,'' in Proceedings of the IEEE International Symposium on Circuits (ISCAS'98) 2, pp. 204-207,1998.

    Google Scholar 

  34. 34.

    B. A. Minch, C. Diorio, P. Hasler, and C. MeadThe matching of small capacitors for analog VLSI,'' in Proceedings of the IEEE International Symposium on Circuits (ISCAS'96) 1, pp.239-241,1996.

    Google Scholar 

  35. 35.

    A. Pavasovic¬, A. G. Andreou, and C. R. WestgateCharacterization of subthreshold MOS mismatch in transistors for VLSI systems.'' Analog Integrated Circuits and Signal Processing 6, pp.75-85,1994.

    Google Scholar 

  36. 36.

    M. F. LandVisual acuity in insects.'' Annual Rev. Entomol. 42, pp.147-177,1997.

    Google Scholar 

  37. 37.

    J. M. ZankerOn the directional sensitivity of motion detectors.'' Biological Cybernetics 62, pp.177-183,1990.

    Google Scholar 

  38. 38.

    D. Marr, Vision. New York, W.H. Freeman & Co.,1982.

    Google Scholar 

  39. 39.

    K. Go» tzDie optischen U» bertragungseigenschaften der Komplexaugen von Drosophila,'' Kybernetik 2, pp.215-221, 1965.

    Google Scholar 

  40. 40.

    R. R. de Ruyter van Steveninck, W. H. Zaagman, and H. A. K. MastebroekAdaptation of Transient Responses of a Movement-Sensitive Neuron in the Visual System of the BlowØy Calliphora erythrocephala.'' Biological Cybernetics 54, pp.223-236,1986.

    Google Scholar 

  41. 41.

    D. L. Ruderman and W. BialekStatistics of natural images: scaling in the woods.'' Physic Review Letters 73, pp.814-817, 1994.

    Google Scholar 

  42. 42.

    D. W. Dong and J. J. AtickStatistics of natural time-varying images.'' Network 6, pp.345-358,1995.

    Google Scholar 

  43. 43.

    B. Klaus and P. Horn, Robot Vision. Cambridge, MA, MIT Press,1986.

    Google Scholar 

  44. 44.

    R. R. Harrison and C. KochA robust analog VLSI motion sensor based on the visual system of the Øy.'' Auton. Robots 7, pp.211-224,1999.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Harrison, R.R., Koch, C. A Robust Analog VLSI Reichardt Motion Sensor. Analog Integrated Circuits and Signal Processing 24, 213–229 (2000). https://doi.org/10.1023/A:1008361525235

Download citation

  • Reichardt motion detector
  • analog VLSI
  • insect vision
  • motion sensor
  • robust sensing
  • biological model