BMVC92 pp 377-386 | Cite as

Layered Architecture for the Control of Micro Saccadic Tracking of a Stereo Camera Head

  • J. E. W. Mayhew
  • Y. Zheng
  • S. A. Billings
Conference paper


The paper describes a 3-layered architecture for the control of the stereoscopic eye-saccade system of a stereo-camera head1 mounted on an autonomous vehicle.

The 02-level is a proportional feedback controller providing a microsaccadic 2 control for eye movements enabling the head to foveate and track targets but requiring iteration through the vision system with the attendant computational overhead.

The 1-level provides the feedforward inverse kinematics for saccadic eye movements allowing a ballistic movement to replace the 0-level control loop. The training data is provided by the feedback error signal from the 0-level controller.

The 2-level is an adaptive lattice filter which is used to track moving targets. The filter is ‘trained’ using vision error-feedback from previous saccades. The filter learns to predict the future target position in the next image. This is used by the inverse kinematics module to generate the eye movement commands for the appropriate predictive saccade.


Inverse Kinematic Target Velocity Stereo Camera Target Trajectory Lattice Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Alexander, S. T., (1986) Adaptive Signal Processing, Springer-Varlag, New York.MATHGoogle Scholar
  2. [2]
    Carpenter, R. H. S., (1988) Movements of the Eyes. Pion, London.Google Scholar
  3. [3]
    Dean, P., Mayhew, J. E. W., Thacker, N., and Langdon, P. M. (1991), Saccade control in a simulated robot camera-head system: neural net architectures for efficient learning of inverse kinematics. Biological Cybernetics, 66, 27–36.CrossRefGoogle Scholar
  4. [4]
    Goodwin, G. C, Sin, K. S., (1984) Adaptive Filtering, Prediction and Control, Prentice-Hall, New Jersey.MATHGoogle Scholar
  5. [5]
    Kawato, M., (1989) Neural network models for formation and control of multijoint arm trajectory. In: Ito M. (ed) Neural programming. Taniguchi Symposia on Brain Sciences No 12. Japan Scientific Society Press/Karger, Basel, pp 189–201.Google Scholar
  6. [6]
    Makhoul, J., (April, 1975) Linear Prediction: A Tutorial Review, Proc. IEEE, Vol. 63, No.4.CrossRefGoogle Scholar
  7. [7]
    Mayhew, J. E. W., (1992) ANIT: Architecture for navigation and intelligent tracking (in preparation).Google Scholar
  8. [8]
    Mayhew, J. E. W., Dean, P., Langdon, P., (1992) Artificial neural networks for the kinematic control of a stereo camera head (in preparation).Google Scholar
  9. [9]
    Widrow, B. and Stearns, S. D., (1985) Adaptive Signal Processing, Prentice-Hall, New Jersey.MATHGoogle Scholar
  10. [10]
    Zheng, Y., Mayhew, J. E. W., Billings, S. A., and Frisby, J. P., (1991) Lattice predictor for 3D vision and intelligent tracking. AIVRU Memo No 67.Google Scholar

Copyright information

© Springer-Verlag London Limited 1992

Authors and Affiliations

  • J. E. W. Mayhew
    • 1
  • Y. Zheng
    • 2
  • S. A. Billings
    • 2
  1. 1.Artificial Intelligence Vision Research UnitUniversity of SheffieldSheffieldEngland
  2. 2.Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldEngland

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