Omnidirectional Image Stabilization by Computing Camera Trajectory

  • Akihiko Torii
  • Michal Havlena
  • Tomáš Pajdla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


In this paper we present a pipeline for camera pose and trajectory estimation, and image stabilization and rectification for dense as well as wide baseline omnidirectional images. The input is a set of images taken by a single hand-held camera. The output is a set of stabilized and rectified images augmented by the computed camera 3D trajectory and reconstruction of feature points facilitating visual object recognition. The paper generalizes previous works on camera trajectory estimation done on perspective images to omnidirectional images and introduces a new technique for omnidirectional image rectification that is suited for recognizing people and cars in images. The performance of the pipeline is demonstrated on a real image sequence acquired in urban as well as natural environments.


Structure from Motion Omnidirectional Vision 


  1. 1.
    Akbarzadeh, A., Frahm, J.M., Mordohai, P., Clipp, B., Engels, C., Gallup, D., Merrell, P., Phelps, M., Sinha, S., Talton, B., Wang, L., Yang, Q., Stewénius, H., Yang, R., Welch, G., Towles, H., Nistér, D., Pollefeys, M.: Towards urban 3d reconstruction from video. In: 3DPVT (May 2006) (invited paper)Google Scholar
  2. 2.
    Cornelis, N., Cornelis, K., Van Gool, L.: Fast compact city modeling for navigation pre-visualization. In: CVPR 2006, pp. II:1339–II:1344 (2006)Google Scholar
  3. 3.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  4. 4.
    Goedemé, T., Nuttin, M., Tuytelaars, T., Van Gool, L.: Omnidirectional vision based topological navigation. IJCV 74(3), 219–236 (2007)CrossRefGoogle Scholar
  5. 5.
    Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. In: CVPR, vol. 2, pp. 2137–2144 (June 2006)Google Scholar
  6. 6.
    Leibe, B., Cornelis, N., Cornelis, K., Van Gool, L.: Dynamic 3d scene analysis from a moving vehicle. In: CVPR 2007, Minneapolis, MN, USA (2007)Google Scholar
  7. 7.
    Leibe, B., Schindler, K., Van Gool, L.: Coupled detection and trajectory estimation for multi-object tracking. In: ICCV 2007 (2007)Google Scholar
  8. 8.
    Torii, A., Havlena, M., Pajdla, T., Leibe, B.: Measuring camera translation by the dominant apical angle. In: CVPR 2008, Anchorage, AK, USA (2008)Google Scholar
  9. 9.
    2d3 Boujou (2001),
  10. 10.
    Mičušík, B., Pajdla, T.: Structure from motion with wide circular field of view cameras. IEEE Trans. PAMI 28(7), 1135–1149 (2006)CrossRefGoogle Scholar
  11. 11.
    Bakstein, H., Pajdla, T.: Panoramic mosaicing with a 180° field of view lens. In: Proc. IEEE Workshop on Omnidirectional Vision, pp. 60–67 (2002)Google Scholar
  12. 12.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22(10), 761–767 (2004)CrossRefGoogle Scholar
  13. 13.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. IJCV 65(1-2), 43–72 (2005)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Obdržálek, Š., Matas, J.: Object recognition using local affine frames on distinguished regions. In: BMVC 2002, London, UK, vol. 1, pp. 113–122 (2002)Google Scholar
  16. 16.
    Obdržálek, Š., Matas, J.: Image retrieval using local compact DCT-based representation. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 490–497. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Fischler, M., Bolles, R.: 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
  18. 18.
    Chum, O., Matas, J.: Matching with PROSAC - progressive sample consensus. In: CVPR 2005, Los Alamitos, USA, vol. 1, pp. 220–226 (2005)Google Scholar
  19. 19.
    Nistér, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. PAMI 26(6), 756–770 (2004)CrossRefGoogle Scholar
  20. 20.
    Stewénius, H.: Gröbner Basis Methods for Minimal Problems in Computer Vision. PhD thesis, Centre for Mathematical Sciences LTH, Lund University, Sweden (2005)Google Scholar
  21. 21.
    Li, H., Hartley, R.: A non-iterative method for correcting lens distortion from nine point correspondences. In: OMNIVIS 2005 (2005)Google Scholar
  22. 22.
    Kahl, F.: Multiple view geometry and the L-infinity norm. In: ICCV (2005)Google Scholar
  23. 23.
    Ke, Q., Kanade, T.: Quasiconvex optimization for robust geometric reconstruction. IEEE Trans. PAMI 29(10), 1834–1847 (2007)CrossRefGoogle Scholar
  24. 24.
    Lourakis, M., Argyros, A.: The design and implementation of a generic sparse bundle adjustment software package based on the levenberg-marquardt algorithm. Technical Report 340, Institute of Computer Science - FORTH, Heraklion, Crete, Greece (August 2004),

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Akihiko Torii
    • 1
  • Michal Havlena
    • 1
  • Tomáš Pajdla
    • 1
  1. 1.Center for Machine Perception, Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical University in PraguePrague 2Czech Republic

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