Advertisement

Puzzle Approach to Pose Tracking of a Rigid Object in a Multi Camera System

  • Sönke SchmidEmail author
  • Xiaoyi Jiang
  • Klaus Schäfers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)

Abstract

Optical tracking is a large field of research with countless sophisticated methods for a multitude of applications. However, there always exist tasks with special requirements and constraints that are not covered by traditional methods. This work presents a puzzle-based approach to tackle the problem of tracking all 6 degrees of freedom of a rigid object with few trackable features using a multi camera system. The presented algorithm capitalizes on non-sequential processing to assemble tracking information bit by bit. Validation shows that it achieves very high accuracy on real data.

Keywords

High accuracy tracking Rigid body Offline processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schmid, S., Jiang, X., Schäfers, K.: High-precision lens distortion correction using smoothed thin plate splines. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 432–439. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  2. 2.
    Walker, M.W., Shao, L., Volz, R.A.: Estimating 3-D Location Parameters Using Dual Number Quaternions. CVGIP: Image Underst. 54, 358–367 (1991)zbMATHCrossRefGoogle Scholar
  3. 3.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2004)Google Scholar
  4. 4.
    Park, F.C., Ravani, B.: Bézier Curves on Riemannian Manifolds and Lie Groups with Kinematic Applications. Trans. ASME, Journal of Mechanical Design 117, 36–40 (1995)CrossRefGoogle Scholar
  5. 5.
    Risse, B., Berh, D., Tao, J., Jiang, X.: Comparison of two 3D Tracking Paradigms for Freely Flying Insects. EURASIP Journal on Image and Video Processing 57 (2013)Google Scholar
  6. 6.
    Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Comput. Surv. 38 (2006)Google Scholar
  7. 7.
    Schmid, S., Dawood, M., Frohwein, L., Jiang, X., Schäfers, K.P.: Camera-based high accuracy tracking system for freely moving mice inside the quadHIDAC pet scanner. In: Medical Imaging Conference, Anaheim, CA, USA (2012)Google Scholar
  8. 8.
    Caljon, T., Enescu, V., Schelkens, P., Sahli, H.: An offline bidirectional tracking scheme. In: Blanc-Talon, J., Philips, W., Popescu, D.C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 587–594. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  9. 9.
    Jiang, X., Dawood, M., Gigengack, F., Risse, B., Schmid, S., Tenbrinck, D., Schäfers, K.: Biomedical imaging: a computer vision perspective. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part I. LNCS, vol. 8047, pp. 1–19. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  10. 10.
    Smeulders, A.W.M., et al.: Visual Tracking: An Experimental Survey. IEEE Trans. on Pattern Analysis and Machine Intelligence 36, 1442–1468 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sönke Schmid
    • 1
    • 2
    • 3
    Email author
  • Xiaoyi Jiang
    • 1
    • 2
    • 3
  • Klaus Schäfers
    • 2
    • 3
  1. 1.Department of Mathematics and Computer ScienceUniversity of MünsterMünsterGermany
  2. 2.European Institute for Molecular ImagingUniversity of MünsterMünsterGermany
  3. 3.Cluster of Excellence EXC 1003, Cells in Motion, CiMMünsterGermany

Personalised recommendations