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Model-Based Motion Capture for Crash Test Video Analysis

  • Juergen Gall
  • Bodo Rosenhahn
  • Stefan Gehrig
  • Hans-Peter Seidel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5096)

Abstract

In this work, we propose a model-based approach for estimating the 3D position and orientation of a dummy’s head for crash test video analysis. Instead of relying on photogrammetric markers which provide only sparse 3D measurements, features present in the texture of the object’s surface are used for tracking. In order to handle also small and partially occluded objects, the concepts of region-based and patch-based matching are combined for pose estimation. For a qualitative and quantitative evaluation, the proposed method is applied to two multi-view crash test videos captured by high-speed cameras.

Keywords

IEEE Conf Background Clutter Occlude Object Segmented Contour Engine Hood 
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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References

  1. 1.
    Gall, J., Rosenhahn, B., Seidel, H.P.: Clustered stochastic optimization for object recognition and pose estimation. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 32–41. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Hogg, D.: Model-based vision: A program to see a walking person. Image and Vision Computing 1(1), 5–20 (1983)CrossRefGoogle Scholar
  3. 3.
    Gavrila, D., Davis, L.: 3-d model-based tracking of humans in action: a multi-view approach. In: IEEE Conf. on Comp. Vision and Patt. Recog., pp. 73–80 (1996)Google Scholar
  4. 4.
    Bregler, C., Malik, J., Pullen, K.: Twist based acquisition and tracking of animal and human kinematics. Int. J. of Computer Vision 56(3), 179–194 (2004)CrossRefGoogle Scholar
  5. 5.
    Rosenhahn, B., Brox, T., Weickert, J.: Three-dimensional shape knowledge for joint image segmentation and pose tracking. Int. Journal of Computer Vision 73(3), 243–262 (2007)CrossRefGoogle Scholar
  6. 6.
    Brox, T., Rosenhahn, B., Cremers, D., Seidel, H.P.: High accuracy optical flow serves 3-d pose tracking: Exploiting contour and flow based constraints. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 98–111. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Lepetit, V., Pilet, J., Fua, P.: Point matching as a classification problem for fast and robust object pose estimation. In: IEEE Conf. on Computer Vision and Patt. Recognition, pp. 244–250 (2004)Google Scholar
  8. 8.
    Li, H., Roivainen, P., Forcheimer, R.: 3-d motion estimation in model-based facial image coding. IEEE Trans. Pattern Anal. Mach. Intell. 15(6) (1993)Google Scholar
  9. 9.
    Gall, J., Rosenhahn, B., Seidel, H.P.: Robust pose estimation with 3d textured models. In: Chang, L.-W., Lie, W.-N. (eds.) PSIVT 2006. LNCS, vol. 4319, pp. 84–95. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Gehrig, S., Badino, H., Paysan, P.: Accurate and model-free pose estimation of small objects for crash video analysis. In: Britsh Machine Vision Conference (2006)Google Scholar
  11. 11.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Conf. on Comp. Vision and Patt. Recog., pp. 593–600 (1994)Google Scholar
  12. 12.
    Lowe, D.: Object recognition from local scale-invariant features. In: Int. Conf. on Computer Vision, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Ke, Y., Sukthankar, R.: Pca-sift: A more distinctive representation for local image descriptors. In: IEEE Conf. on Comp. Vision and Patt. Recog., vol. 2, pp. 506–513 (2004)Google Scholar
  14. 14.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: IEEE Conf. on Computer Vision and Patt. Recognition, pp. 257–263 (2003)Google Scholar
  15. 15.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. Journal of Computer Vision 13(2), 119–152 (1994)CrossRefGoogle Scholar
  16. 16.
    Stolfi, J.: Oriented Projective Geometry: A Framework for Geometric Computation. Academic Press, Boston (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Juergen Gall
    • 1
  • Bodo Rosenhahn
    • 1
  • Stefan Gehrig
    • 2
  • Hans-Peter Seidel
    • 1
  1. 1.Max-Planck-Institute for Computer ScienceSaarbrückenGermany
  2. 2.Daimler AG, Environment PerceptionSindelfingenGermany

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