Skip to main content

Estimating 3D Human Body Pose from Stereo Image Sequences

  • Conference paper
Gesture in Human-Computer Interaction and Simulation (GW 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3881))

Included in the following conference series:

  • 1178 Accesses

Abstract

This paper presents a novel method for estimating 3D human body pose from stereo image sequences based on top-down learning. Human body pose is represented by a linear combination of prototypes of 2D depth images and their corresponding 3D body models in terms of the position of a predetermined set of joints. With a 2D depth image, we can estimate optimal coefficients for a linear combination of prototypes of the 2D depth images by solving least square minimization. The 3D body model of the input depth image is obtained by applying the estimated coefficients to the corresponding 3D body model of prototypes. In the learning stage, the proposed method is hierarchically constructed by classifying the training data into several clusters with a silhouette images and a depth images recursively. Also, in the estimating stage, the proposed method hierarchically estimates 3D human body pose with a silhouette image and a depth image. The experimental results show that our method can be efficient and effective for estimating 3D human body pose.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agarwal, A., Triggs, B.: 3D Human Pose From Silhouette by Relevance Vector Regression. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington D.C., USA, July 2004, pp. 882–888 (2004)

    Google Scholar 

  2. Bowden, R., Mitchell, T.A., Sarhadi, M.: Non-linear Statistical Models for 3D Reconstruction of Human Pose and Motion from Monocular Image Sequences. Image and Vision Computing 18, 729–737 (2000)

    Article  Google Scholar 

  3. Hwang, B.-W., Kim, S., Lee, S.-W.: 2D and 3D Full-Body Gesture Database for Analyzing Daily Human Gestures. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 611–620. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Ong, E.J., Gong, S.: A Dynamic Human Model Using Hybrid 2D-3D Representations in Hierarchical PCA Space. In: Proc. of 10th British Machine Vision Conference, Nottingham, UK, September 1999, pp. 33–42 (1999)

    Google Scholar 

  5. Rosales, R., Sclaroff, S.: Specialized Mapping and the Estimation of Human Body Pose from a Single Image. In: Proc. of IEEE Workshop on Human Motion, Texas, USA, December 2000, pp. 19–24 (2000)

    Google Scholar 

  6. Yang, H.-D., Park, S.-K., Lee, S.-W.: Reconstruction of 3D Human Body Pose Based on Top-Down Learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 601–610. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, HD., Park, SK., Lee, SW. (2006). Estimating 3D Human Body Pose from Stereo Image Sequences. In: Gibet, S., Courty, N., Kamp, JF. (eds) Gesture in Human-Computer Interaction and Simulation. GW 2005. Lecture Notes in Computer Science(), vol 3881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11678816_20

Download citation

  • DOI: https://doi.org/10.1007/11678816_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32624-3

  • Online ISBN: 978-3-540-32625-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics