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Detecting Incorrect Feature Tracking by Affine Space Fitting

  • Chika Takada
  • Yasuyuki Sugaya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

We present a new method for detecting incorrect feature point tracking. In this paper, we detect incorrect feature point tracking by imposing the constraint that under the affine camera model feature trajectories should be in an affine space in the parameter space. Introducing a statistical model of image noise, we test detected partial trajectories are sufficiently reliable. Then we detect incorrect partial trajectories. Using real video images, we demonstrate that our proposed method can detect incorrect feature point tracking fairly well.

References

  1. 1.
    Brandt, S.: Closed-form solutions for affine reconstruction under missing data. In: Proc. Statistical Methods in Video Processing Workshop, Copenhagen, Denmark, June 2002, pp. 109–114 (2002)Google Scholar
  2. 2.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Hartley, R., Horseman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)Google Scholar
  4. 4.
    Huynh, D.Q., Heyden, A.: Outlier detection in video sequences under affine projection. In: Proc. IEEE Conf. Comput. Vision Pattern Recog., Kauai, HI, U.S.A, December 2001, vol. 2, pp. 695–701 (2001)Google Scholar
  5. 5.
    Ichimura, N.: Stochastic filtering for motion trajectory in image sequences using a Monte Carlo filter with estimation of hyper-parameters. In: Proc. 16th Int. Conf. Pattern Recog., Quebec City, Canada, August 2002, vol. 4, pp. 68–73 (2002)Google Scholar
  6. 6.
    Ichimura, N., Ikoma, N.: Filtering and smoothing for motion trajectory of feature point using non-gaussian state space model. IEICE Trans. Inf. Syst. E84-D(6), 755–759 (2001)Google Scholar
  7. 7.
    Jacobs, D.W.: Linear fitting with missing data for structure-from-motion. Comput. Vision Image Understand. 82(1), 57–81 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Kanatani, K.: Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier Scence, Amsterdam (1996)zbMATHGoogle Scholar
  9. 9.
    Kanatani, K.: Motion segmentation by subspace separation and model selection. In: Proc. 8th Int. Conf. Comput. Vision, Vancouver, Canada, vol. 2, pp. 301–306 (2001)Google Scholar
  10. 10.
    Kanatani, K.: Motion segmentation by subspace separation: Model selection and reliability evaluation. Int. J. Image Graphics 2(2), 179–197 (2002)CrossRefGoogle Scholar
  11. 11.
    Kanatani, K.: Evaluation and selection of models for motion segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 335–349. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Poelman, C.J., Kanade, T.: A paraperspective factorization method for shape and motion recovery. IEEE Trans. Patt. Anal. Mach. Intell. 19(3), 206–218 (1997)CrossRefGoogle Scholar
  13. 13.
    Saito, H., Kamijima, S.: Factorization method using interpolated feature tracking via projective geometry. In: Proc. 14th British Machine Vision Conf., Norwich, UK, vol. 2, pp. 449–458 (September 2003)Google Scholar
  14. 14.
    Sugaya, Y., Kanatani, K.: Automatic camera model selection for multibody motion segmentation. In: Proceedings of the IAPR Workshop on Machine Vision Applications (MVA 2002), Nara, Japan, 11-13 December, pp. 412–415 (2002)Google Scholar
  15. 15.
    Sugaya, Y., Kanatani, K.: Outlier removal for motion tracking by subspace separation. IEICE Trans. Inf. & Syst. E86-D(6), 1095–1102 (2003)Google Scholar
  16. 16.
    Sugaya, Y., Kanatani, K.: Extending interrupted feature point tracking for 3-D affine reconstruction. IEICE Transactions on Information and Systems E87-D(4), 1031–1038 (2004)zbMATHGoogle Scholar
  17. 17.
    Sugaya, Y., Kanatani, K.: Multi-stage optimization for multi-body motion segmentation. IEICE Transactions on Information and Systems E87-D(7), 1935–1942 (2004)Google Scholar
  18. 18.
    Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography—A factorization method. Int. J. Comput. Vision 9(2), 137–154 (1992)CrossRefGoogle Scholar
  19. 19.
    Tomasi, C., Kanade, T.: Detection and Tracking of Point Features, CMU Tech. Rep. CMU-CS-91-132 (April 1991), http://www.ces.clemson.edu/~stb/klt/

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chika Takada
    • 1
  • Yasuyuki Sugaya
    • 1
  1. 1.Department of Information and Computer SciencesToyohashi University of TechnologyToyohashiJapan

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