From Saliency to Eye Gaze: Embodied Visual Selection for a Pan-Tilt-Based Robotic Head

  • Matei Mancas
  • Fiora Pirri
  • Matia Pizzoli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

This paper introduces a model of gaze behavior suitable for robotic active vision. Built upon a saliency map taking into account motion saliency, the presented model estimates the dynamics of different eye movements, allowing to switch from fixational movements, to saccades and to smooth pursuit. We investigate the effect of the embodiment of attentive visual selection in a pan-tilt camera system. The constrained physical system is unable to follow the important fluctuations characterizing the maxima of a saliency map and a strategy is required to dynamically select what is worth attending and the behavior, fixation or target pursuing, to adopt. The main contributions of this work are a novel approach toward real time, motion-based saliency computation in video sequences, a dynamic model for gaze prediction from the saliency map, and the embodiment of the modeled dynamics to control active visual sensing.

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References

  1. 1.
    Treisman, A., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12, 97–136 (1980)CrossRefGoogle Scholar
  2. 2.
    Tsotsos, J., Culhane, S., Wai, W., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artifical Intelligence 78, 507–547 (1995)CrossRefGoogle Scholar
  3. 3.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI 20, 1254–1259 (1998)CrossRefGoogle Scholar
  4. 4.
    Butko, N., Movellan, J.: Infomax control of eye movements. IEEE Transactions on Autonomous Mental Development 2, 91–107 (2010)CrossRefGoogle Scholar
  5. 5.
    Koch, C., Ullman, S.: Shifts in selective visual-attention: towards the underlying neural circuitry. Hum. Neurobiol. 4, 219–227 (1985)Google Scholar
  6. 6.
    Mahadevan, V., Vasconcelos, N.: Spatiotemporal saliency in dynamic scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 171–177 (2010)CrossRefGoogle Scholar
  7. 7.
    Kowler, E.: Eye movements: The past 25years. Vision Research, 1–27 (2011)Google Scholar
  8. 8.
    Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Mancas, M., Riche, N., Leroy, J., Gosselin, B.: Abnormal motion selection in crowds using bottom-up saliency. In: Proc. of the ICIP (2011)Google Scholar
  10. 10.
    Sauter, D., Martin, B., Di Renzo, N., Vomscheid, C.: Analysis of eye tracking movements using innovations generated by a kalman filter. Medical and Biological Engineering and Comp. (1991)Google Scholar
  11. 11.
    Engbert, R., Kliegl, R.: Microsaccades uncover the orientation of covert attention. Vision Research 43, 1035–1045 (2003)CrossRefGoogle Scholar
  12. 12.
    Komogortsev, O., Khan, J.I.: Eye movement prediction by kalman filter with integrated linear horizontal oculomotor plant mechanical model. In: ETRA, pp. 229–236 (2008)Google Scholar
  13. 13.
    Blom, H., Bar-Shalom, Y.: The interactive multiple model algorithm for system with markovian switching coefficients. IEEE Trans. on Automatic Control 33, 780–783 (1988)CrossRefMATHGoogle Scholar
  14. 14.
    Julier, S.J., Jeffrey, Uhlmann, K.: Unscented filtering and nonlinear estimation. Proceedings of the IEEE (2004)Google Scholar
  15. 15.
    Julier, S.J., Uhlmann, J.K.: A new extension of the kalman filter to nonlinear systems. In: Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, pp. 182–193 (1997)Google Scholar
  16. 16.
    Wan, E., van der Merwe, R.: The unscented kalman filter for nonlinear estimation. In: Proc. of the Symposium on Adaptive Systems for Signal Processing, Communication and Control (2000)Google Scholar
  17. 17.
    Singer, R.A.: Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Transactions on Aerospace and Electrictronic Systems (1970)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Matei Mancas
    • 1
  • Fiora Pirri
    • 2
  • Matia Pizzoli
    • 2
  1. 1.University of MonsMonsBelgium
  2. 2.Sapienza Università di RomaRomeItaly

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