SVR-Based Jitter Reduction for Markerless Augmented Reality

  • Samuele Salti
  • Luigi Di Stefano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


The ability to augment a video stream with consistent virtual contents is an attractive Computer Vision application. The first Augmented Reality (AR) proposals required the scene to be endowed with special markers. Recently, thanks to the developments in the field of natural invariant local features, similar results have been achieved in a markerless scenario. The computer vision community is now equipped with a set of relatively standard techniques to solve the underlying markerless camera pose estimation problem, at least for planar textured reference objects. The majority of proposals, however, does not exploit temporal consistency across frames in order to reduce some disturbing effects of per-frame estimation, namely visualization of short spurious estimations and jitter. We proposes a new method based on Support Vector Regression to mitigate these undesired effects while preserving the ability to work in real-time. Our proposal can be used as a post processing step independent of the chosen pose estimation method, thus providing an effective and easily integrable building block for AR applications.


Augmented Reality Support Vector Regression Current Frame Augmented Reality System Augmented Reality Application 
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.


  1. 1.
    Gordon, I., Lowe, D.G.: What and where: 3d object recognition with accurate pose. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 67–82. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Lepetit, V., Vacchetti, L., Thalmann, D., Fua, P.: Fully automated and stable registration for augmented reality applications. In: ISMAR 2003, Washington, DC, USA, p. 93. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  3. 3.
    Cornelis, K., Pollefeys, M., Van Gool, L.: Tracking based structure and motion recovery for augmented video productions. In: VRST 2001, pp. 17–24. ACM, New York (2001)Google Scholar
  4. 4.
    Chia, K.W., Cheok, A.D., Prince, S.J.D.: Online 6 dof augmented reality registration from natural features. In: ISMAR 2002: Int. Symp. on Mixed and Augmented Reality, Washington, DC, USA, p. 305. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar
  5. 5.
    Chai, L., Hoff, B., Vincent, T., Nguyen, K.: An adaptive estimator for registration in augmented reality. In: International Workshop on Augmented Reality, vol. 0, p. 23 (1999)Google Scholar
  6. 6.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Moreno-Noguer, F., Lepetit, V., Fua, P.: Accurate non-iterative o(n) solution to the pnp problem. In: IEEE Int. Conf. on Computer Vision, Rio de Janeiro, Brazil (October 2007)Google Scholar
  8. 8.
  9. 9.
    Schweighofer, G., Pinz, A.: Robust pose estimation from a planar target. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(12), 2024–2030 (2006)CrossRefGoogle Scholar
  10. 10.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Smola, A.J., Olkopf, B.S.: A tutorial on support vector regression. Technical report, Statistics and Computing (1998)Google Scholar
  12. 12.
    Korn, G.A., Korn, T.M.: Mathematical Handbook for Scientists and Engineers. McGraw Hill, New York (1968)zbMATHGoogle Scholar
  13. 13.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Hess, R.: (last visited 19/01/2009)
  15. 15.
    Azzari, P., di Stefano, L.: Vision-based markerless gaming interface. In: ICIAP 2009 (2009)Google Scholar
  16. 16.
    Azzari, P., di Stefano, L., Tombari, F., Mattoccia, S.: Markerless augmented reality using image mosaics. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 413–420. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Samuele Salti
    • 1
  • Luigi Di Stefano
    • 1
  1. 1.DEISUniversity of BolognaBolognaItaly

Personalised recommendations