Skip to main content
SpringerLink
Log in

Variable structure multiple model for articulated human motion tracking from monocular video sequences

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

A new model-based human body tracking framework with learning-based theory is introduced in this paper. We propose a variable structure multiple model (VSMM) framework to address challenging problems such as uncertainty of motion styles, imprecise detection of feature points, and ambiguity of joint locations. Key human joint points are detected automatically and the undetected points are estimated with Kalman filters. Multiple motion models are learned from motion capture data using a ridge regression method. The model set that covers the total motion set is designed on the basis of topological and compatibility relationships, while the VSMM algorithm is used to estimate quaternion vectors of joint rotation. Experiments using real image sequences and simulation videos demonstrate the high efficiency of our proposed human tracking framework.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Mikic I, Trivedi M, Hunter E, et al. Human body model acquisition and tracking using voxel data. Int J Comput Vision, 2003, 53: 199–223

    Article  Google Scholar 

  2. Xu X Y, Li B X. Learning motion correlation for tracking articulated human body with a Rao-Blackwellised particle filter. In: Proceedings of the International ConferenceOn Computer Vision (ICCV07). Rio de Janeiro, 2007. 1–8

  3. Sigal L, Blan A, Black M J. HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int J Comput Vision, 2010, 87: 1–2

    Article  Google Scholar 

  4. Sigal L, Balan A, Black M J. HumanEva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int J Comput Vision, 2010, 87: 4–27

    Article  Google Scholar 

  5. Tong M L. Three dimensional human motion analysis from uncalibrated monocular image sequences (in Chinese). PhD Thesis. ShangHai: Shanghai Jiao Tong University, 2007. 71–91

    Google Scholar 

  6. Bo L F, Sminchisescu C. Structured output-associative regression. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009. 2403–2410

  7. Bo L F, Sminchisescu C. Twin Gaussian processes for structured prediction. Int J Comput Vision, 2010, 87: 28–52

    Article  Google Scholar 

  8. Lee C S, Elgammal A. Coupled visual and kinematic manifold models for tracking. Int J Comput Vision, 2010, 87: 118–139

    Article  Google Scholar 

  9. Li X R, Zhi X R, Zhang Y M. Multiple-model estimation with variable structure-part III: model-group switching algorithm. IEEE Trans Aero Elec Sys, 1999, 35: 225–241

    Article  Google Scholar 

  10. Han H, Yue L C, Jiao L C, et al. Human body motion tracking based on quantum-inspired immune cloning algorithm. P SPIE, 2009, 7496: 1–8

    Google Scholar 

  11. Wang L, Tan T N, Ning H Z, et al. Silhouette analysis based gait recognition for human identification. IEEE Trans Pattern Anal, 2003, 25: 1505–1518

    Article  Google Scholar 

  12. Sandhu R, Dambreville S. A nonrigid kernel-based framework for 2D-3D pose estimation and 2D image segmentation. IEEE Trans Pattern Anal, 2011, 33: 1098–1115

    Article  Google Scholar 

  13. Gupta A, Chen T. Context and observation driven latent variable model for human pose estimation. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR08), Anchorage, 2008. 1–8

  14. Okada R, Soatto S. Relevant feature selection for human pose estimation and localization in cluttered images. LNCS, 2008, 5303: 434–445

    Google Scholar 

  15. Urtasun R, Darrell T. Local probabilistic regression for activity-independent human pose inference. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, 2008. 1–8

  16. Grauman K, Shakhnarovich G, Darrell T. Inferring 3D structure with a statistical image based shape model. In: Proceedings of IEEE International Conference on Computer Vision, Nice, 2003. 641–647

  17. Li X R, Zhang Y M, Zhi X R. Multiple-model estimation with variable structure-part IV: design and evaluation of model-group switching algorithm. IEEE Trans Aero Elec Sys, 1999, 35: 242–254

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071, China

    Hong Han, ZhiChao Chen & YouJian Fan

  2. School of Computer and Information Engineering, Shanghai University of Electric Power, Shanghai, 200090, China

    MingLei Tong

Authors
  1. Hong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. MingLei Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. ZhiChao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. YouJian Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Han.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Han, H., Tong, M., Chen, Z. et al. Variable structure multiple model for articulated human motion tracking from monocular video sequences. Sci. China Inf. Sci. 55, 1138–1150 (2012). https://doi.org/10.1007/s11432-011-4529-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-011-4529-8

Keywords

Search

Navigation