Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Conference paper

Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 3952))

Included in the following conference series:

  • 5538Accesses

Abstract

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.

Similar content being viewed by others

Keywords

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.

References

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  4. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  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. Lowe, D.G.: Fitting parameterized three-dimensional models to image. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(5), 441–450 (1991)

    Article  Google Scholar 

  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. 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. Theuwissen, A.J.P.: Solid-state imaging with chargecoupled devices. Kluwer Academic Publishers, Dordrecht (1995)

    Google Scholar 

  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. 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. Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université Blaise Pascal Clermont Ferrand, LASMEA UMR 6602 CNRS, France

    Omar Ait-Aider, Nicolas Andreff, Jean Marc Lavest & Philippe Martinet

Authors
  1. Omar Ait-Aider

    You can also search for this author inPubMed Google Scholar

  2. Nicolas Andreff

    You can also search for this author inPubMed Google Scholar

  3. Jean Marc Lavest

    You can also search for this author inPubMed Google Scholar

  4. Philippe Martinet

    You can also search for this author inPubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Ljubljana, Slovenia

    Aleš Leonardis

  2. Institute for Computer Graphics and Vision, TU Graz, Inffeldgasse 16, 8010, Graz, Austria

    Horst Bischof

  3. Vision-based Measurement Group, Inst. of El. Measurement and Meas. Sign. Proc. Graz, University of Technology, Austria

    Axel Pinz

Rights and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ait-Aider, O., Andreff, N., Lavest, J.M., Martinet, P. (2006). Simultaneous Object Pose and Velocity Computation Using a Single View from a Rolling Shutter Camera. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744047_5

Download citation

Publish with us

[8]ページ先頭
©2009-2025 Movatter.jp