Advertisement

Linking Pose and Motion

  • Andrea Fossati
  • Pascal Fua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

Algorithms designed to estimate 3D pose in video sequences enforce temporal consistency but typically overlook an important source of information: The 3D pose of an object, be it rigid or articulated, has a direct influence on its direction of travel.

In this paper, we use the cases of an airplane performing aerobatic maneuvers and of pedestrians walking and turning to demonstrate that this information can and should be used to increase the accuracy and reliability of pose estimation algorithms.

Keywords

Video Sequence Ground Plane Color Histogram Angle Error Temporal Consistency 
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.
Download to read the full conference paper text

Supplementary material

978-3-540-88693-8_15_MOESM1_ESM.avi (3.3 mb)
Supplementary material(3,348 KB)

References

  1. 1.
    Lepetit, V., Fua, P.: Monocular model-based 3d tracking of rigid objects: A survey. Foundations and Trends in Computer Graphics and Vision (2005)Google Scholar
  2. 2.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. CVIU 104(2), 90–126 (2006)Google Scholar
  3. 3.
    Sidenbladh, H., Black, M.J., Sigal, L.: Implicit Probabilistic Models of Human Motion for Synthesis and Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Bar-Shalom, Y., Kirubarajan, T., Li, X.R.: Estimation with Applications to Tracking and Navigation. John Wiley & Sons, Inc., Chichester (2002)Google Scholar
  5. 5.
    Zexiang, L., Canny, J.: Nonholonomic Motion Planning. Springer, Heidelberg (1993)Google Scholar
  6. 6.
    Ren, L., Patrick, A., Efros, A.A., Hodgins, J.K., Rehg, J.M.: A data-driven approach to quantifying natural human motion. ACM Trans. Graph. 24(3) (2005)Google Scholar
  7. 7.
    Koller, D., Daniilidis, K., Nagel, H.H.: Model-Based Object Tracking in Monocular Image Sequences of Road Traffic Scenes. IJCV 10(3), 257–281 (1993)CrossRefGoogle Scholar
  8. 8.
    Poggio, T., Torre, V., Koch, C.: Computational Vision and Regularization Theory. Nature 317 (1985)Google Scholar
  9. 9.
    Brubaker, M., Fleet, D., Hertzmann, A.: Physics-based person tracking using simplified lower-body dynamics. In: CVPR (2007)Google Scholar
  10. 10.
    Urtasun, R., Fleet, D., Fua, P.: 3D People Tracking with Gaussian Process Dynamical Models. In: CVPR (2006)Google Scholar
  11. 11.
    Ormoneit, D., Sidenbladh, H., Black, M.J., Hastie, T.: Learning and tracking cyclic human motion. In: NIPS (2001)Google Scholar
  12. 12.
    Agarwal, A., Triggs, B.: Tracking articulated motion with piecewise learned dynamical models. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 54–65. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Taycher, L., Shakhnarovich, G., Demirdjian, D., Darrell, T.: Conditional Random People: Tracking Humans with CRFs and Grid Filters. In: CVPR (2006)Google Scholar
  14. 14.
    Rosenhahn, B., Brox, T., Seidel, H.: Scaled motion dynamics for markerless motion capture. In: CVPR (2007)Google Scholar
  15. 15.
    Brox, T., Rosenhahn, B., Cremers, D., Seidel, H.: Nonparametric density estimation with adaptive, anisotropic kernels for human motion tracking. In: Workshop on HUMAN MOTION Understanding, Modeling, Capture and Animation (2007)Google Scholar
  16. 16.
    Howe, N.R., Leventon, M.E., Freeman, W.T.: Bayesian reconstructions of 3D human motion from single-camera video. In: NIPS (1999)Google Scholar
  17. 17.
    Djouadi, A., Snorrason, O., Garber, F.: The quality of training sample estimates of the bhattacharyya coefficient. PAMI 12(1), 92–97 (1990)Google Scholar
  18. 18.
    Isard, M., Blake, A.: CONDENSATION - conditional density propagation for visual tracking. IJCV 29(1), 5–28 (1998)CrossRefGoogle Scholar
  19. 19.
    Fossati, A., Dimitrijevic, M., Lepetit, V., Fua, P.: Bridging the Gap between Detection and Tracking for 3D Monocular Video-Based Motion Capture. In: CVPR (2007)Google Scholar
  20. 20.
    Urtasun, R., Fleet, D., Fua, P.: Temporal Motion Models for Monocular and Multiview 3–D Human Body Tracking. CVIU 104(2-3), 157–177 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andrea Fossati
    • 1
  • Pascal Fua
    • 1
  1. 1.Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

Personalised recommendations

Cite paper