International Conference on Advanced Concepts for Intelligent Vision Systems

Advanced Concepts for Intelligent Vision Systems pp 287-298 | Cite as

Full-Body Human Pose Estimation by Combining Geodesic Distances and 3D-Point Cloud Registration

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9386)

Abstract

In this work, we address the problem of recovering the 3D full-body human pose from depth images. A graph-based representation of the 3D point cloud data is determined which allows for the measurement of pose-independent geodesic distances on the surface of the human body. We extend previous approaches based on geodesic distances by extracting geodesic paths to multiple surface points which are obtained by adapting a 3D torso model to the point cloud data. This enables us to distinguish between the different body parts - without having to make prior assumptions about their locations. Subsequently, a kinematic skeleton model is adapted. Our method does not need any pre-trained pose classifiers and can therefore estimate arbitrary poses.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Le Ly, D., Saxena, A., Lipson, H.: Pose estimation from a single depth image for arbitrary kinematic skeletons. CoRR (2011)Google Scholar
  2. 2.
    Jaeggli, T., Koller-Meier, E., Gool, L.: Learning generative models for multi-activity body pose estimation. Int. J. Comput. Vision 2, 121–134 (2009)CrossRefGoogle Scholar
  3. 3.
    Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., Blake, A.: Efficient human pose estimation from single depth images. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(12), 2821–2840 (2013)CrossRefGoogle Scholar
  4. 4.
    Chang, J.Y., Nam, S.W.: Fast Random-Forest-Based Human Pose Estimation Using a Multi-scale and Cascade Approach 35(6) (2013)Google Scholar
  5. 5.
    Pons-Moll, G., Baak, A., Helten, T., Muller, M., Seidel, H.-P., Rosenhahn, B.: Multisensor-fusion for 3d full-body human motion capture. In: CVPR, pp. 663–670 (2010)Google Scholar
  6. 6.
    Chen, D.C.Y., Fookes, C.B.: Labelled silhouettes for human pose estimation. In: Int. C. on Inform. Science, Signal Proc. a their App. (2010)Google Scholar
  7. 7.
    Srinivasan, K., Porkumaran, K., Sainarayanan, G.: Skin colour segmentation based 2d and 3d human pose modelling using discrete wavelet transform. Pattern Recognit. Image Anal. 21(4), 740–753 (2011)CrossRefGoogle Scholar
  8. 8.
    Liang, Q., Miao, Z.: Markerless human pose estimation using image features and extremal contour. In: ISPACS, pp. 1–4 (2010)Google Scholar
  9. 9.
    Schwarz, L.A., Mkhitaryan, A., Mateus, D., Navab, N.: Human skeleton tracking from depth data using geodesic distances and optical flow. Image Vision Comput. 30(3), 217–226 (2012)CrossRefGoogle Scholar
  10. 10.
    Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. J. of the Optical Society of America 4, 629–642 (1987)CrossRefGoogle Scholar
  11. 11.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)MATHMathSciNetCrossRefGoogle Scholar
  12. 12.
    Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2), 260–269 (1967)MATHCrossRefGoogle Scholar
  13. 13.
    Wang, L.-C.T., Chen, C.C.: A combined optimization method for solving the inverse kinematics problems of mechanical manipulators. IEEE Transactions on Robotics and Automation 7(4), 489–499 (1991)CrossRefGoogle Scholar
  14. 14.
    Rther, M., Straka, M., Hauswiesner, S., Bischof, H.: Skeletal graph based human pose estimation in real-time, pp. 69.1–69.12 (2011). doi: 10.5244/C.25.69

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Information Technology and CommunicationsOtto-von-Guericke-University MagdeburgMagdeburgGermany

Personalised recommendations