Simultaneous Object Pose and Velocity Computation Using a Single View from a Rolling Shutter Camera

  • Omar Ait-Aider
  • Nicolas Andreff
  • Jean Marc Lavest
  • Philippe Martinet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)


An original concept for computing instantaneous 3D pose and 3D velocity of fast moving objects using a single view is proposed, implemented and validated. It takes advantage of the image deformations induced by rolling shutter in CMOS image sensors. First of all, after analysing the rolling shutter phenomenon, we introduce an original model of the image formation when using such a camera, based on a general model of moving rigid sets of 3D points. Using 2D-3D point correspondences, we derive two complementary methods, compensating for the rolling shutter deformations to deliver an accurate 3D pose and exploiting them to also estimate the full 3D velocity. The first solution is a general one based on non-linear optimization and bundle adjustment, usable for any object, while the second one is a closed-form linear solution valid for planar objects. The resulting algorithms enable us to transform a CMOS low cost and low power camera into an innovative and powerful velocity sensor. Finally, experimental results with real data confirm the relevance and accuracy of the approach.


Classical Algorithm Single View Bundle Adjustment Velocity Parameter Velocity Computation 
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.
    Ait-Aider, O., Andreff, N., Lavest, J.M., Martinet, P.: Exploiting rolling shutter distortions for simultaneous object pose and velocity computation using a single view. In: Proc. IEEE International Conference on Computer Vision Systems, New York, USA (January 2006)Google Scholar
  2. 2.
    Dementhon, D., Davis, L.S.: Model-based object pose in 25 lines of code. International Journal of Computer Vision 15(1/2), 123–141 (1995)CrossRefGoogle Scholar
  3. 3.
    Dhome, M., Richetin, M., Lapreste, J.T., Rives, G.: Determination of the attitude of 3-d objects from a single perspective view. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(12), 1265–1278 (1989)CrossRefGoogle Scholar
  4. 4.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)MATHGoogle Scholar
  5. 5.
    Lavest, J.-M., Viala, M., Dhome, M.: Do we really need an accurate calibration pattern to achieve a reliable camera calibration? In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 158–174. Springer, Heidelberg (1998)Google Scholar
  6. 6.
    Lowe, D.G.: Fitting parameterized three-dimensional models to image. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(5), 441–450 (1991)CrossRefGoogle Scholar
  7. 7.
    Meingast, M., Geyer, C., Sastry, S.: Geometric models of rolling-shutter cameras. In: Proc. of the 6th Workshop on Omnidirectional Vision, Camera Networks and Non-Classical Cameras, Beijing, China (October 2005)Google Scholar
  8. 8.
    Phong, T.Q., Horaud, R., Tao, P.D.: Object pose from 2-d to 3-d point and line correspondences. International Journal of Computer Vision, 225–243 (1995)Google Scholar
  9. 9.
    Theuwissen, A.J.P.: Solid-state imaging with chargecoupled devices. Kluwer Academic Publishers, Dordrecht (1995)Google Scholar
  10. 10.
    Tsai, R.Y.: An efficient and accurate camera calibration technique for 3d machine vision. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, pp. 364–374 (1986)Google Scholar
  11. 11.
    Wilburn, B., Joshi, N., Vaish, V., Levoy, M., Horowitz, M.: High-speed videography using a dense camera array. In: IEEE Society Conference on Pattern Recognition, CVPR 2004 (2004)Google Scholar
  12. 12.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  13. 13.
    Zomet, A., Feldman, D., Peleg, S., Weinshall, D.: Mosaicing new views: The crossed-slits projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(6), 741–754 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Omar Ait-Aider
    • 1
  • Nicolas Andreff
    • 1
  • Jean Marc Lavest
    • 1
  • Philippe Martinet
    • 1
  1. 1.Université Blaise Pascal Clermont Ferrand, LASMEA UMR 6602 CNRSFrance

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