OSiMa: Human Pose Estimation from a Single Image

  • Nipun Pande
  • Prithwijit Guha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

Human upper body pose estimation plays a key role in applications related to human-computer interactions. We propose to develop an avatar based video conferencing system where a user’s avatar is animated following his/her gestures. Tracking gestures calls for human pose estimation through image based measurements. Our work is motivated by the pictorial structures approach and we use a 2D model as a collection of rectangular body parts. Stochastic search iterations are used to estimate the angles between these body parts through Orientation Similarity Maximization (OSiMa) along the outline of the body model. The proposed approach is validated on human upper body images with varying levels of background clutter and has shown (near) accurate pose estimation results in real time.

References

  1. 1.
    Moeslund, T., Hilton, A., Kruger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104, 90–126 (2006)CrossRefGoogle Scholar
  2. 2.
    Ramanan, D.: Learning to parse images of articulated bodies. In: Neural Information Processing Systems (NIPS), pp. 1129–1136 (2006)Google Scholar
  3. 3.
    Ferrari, V., Marin-Jimenez, M., Zisserman, A.: Pose search: Retrieving people using their pose. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2009)Google Scholar
  4. 4.
    Freifeld, O., Weiss, A., Zuf, S., Black, M.J.: Contour people: A parameterized model of 2d articulated human shape. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 639–646 (2010)Google Scholar
  5. 5.
    Sapp, B., Jordan, C., Taskar, B.: Adaptive pose priors for pictorial structures. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 422–429 (2010)Google Scholar
  6. 6.
    Yang, W., Wang, Y., Mori, G.: Recognizing human actions from still images with latent poses. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2030–2037 (2010)Google Scholar
  7. 7.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal on Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  8. 8.
    NASA: Anthropometric Source Book, vol. 2. Springfield VA (1978)Google Scholar
  9. 9.
    Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation and Control. Wiley-Interscience, Hoboken (2003)CrossRefMATHGoogle Scholar
  10. 10.
    Ferrari, V., Marin-Jimenez, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  11. 11.
    Craig, J.J.: Introduction to Robotics Mechanics and Control. Pearson Education Inc., London (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nipun Pande
    • 1
  • Prithwijit Guha
    • 1
  1. 1.TCS Innovation LabsNew DelhiIndia

Personalised recommendations