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Pose estimation based on human detection and segmentation

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Abstract

We address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We do not make such assumption. In this paper, a two-step approach is proposed, first, instead of applying background subtraction to get the segmentation of human, we combine the segmentation with human detection using an ISM-based detector. Then, silhouette feature can be extracted and 3D pose estimation is solved as a regression problem. RVMs and ridge regression method are applied to solve this problem. The results show the robustness and accuracy of our method.

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References

  1. Bray M, Kohli P, Torr P. Posecut: Simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts. In: ECCV, 2006

  2. Agarwal A, Triggs B. 3D human pose from silhouettes by relevance vector regression. In: CVPR, 2004

  3. Agarwal A, Triggs B. Recovering 3D human pose from monocular images. IEEE Trans Patt Anal Mach Intell, 2006, 28(1): 44–58

    Article  Google Scholar 

  4. Shakhnarovich G, Viola P, Darrell T. Fast pose estimation with parameter-sensitive hashing. In: ICCV, 2003. 750–757

  5. Kehl R, Bray M, Van Gool L. Full body tracking from multiple views using stochastic sampling. In: CVPR. Volume II, 2005. 129–136

    Google Scholar 

  6. Leibe B, Leonardis A, Schiele B. Combined object categorization and segmentation with an implicit shape model. In: ECCV04. Workshop on Stat. Learning in Computer Vision, May 2004. 17–32

  7. Leibe B, Seemann E, Schiele B. Pedestrian detection in crowded scenes. In: CVPR’05, 2005

  8. Wu B, Nevatia R. Tracking of multiple, partially occluded humans based on static body part detection. CVPR, 2006, 2: 951–958

    Google Scholar 

  9. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: CVPR, 2005

  10. Sminchisescu C, Triggs B. Covariance scaled sampling for monocular 3D body tracking. In: CVPR, 2001. 447–454

  11. Sidenbladh H, Black M J, Fleet D J. Stochastic tracking of 3D human figures using 2D image motion. In: ECCV, 2000. 702–718

  12. Sigal L, Black M. Predicting 3D people from 2D pictures. In: IV Conference on Articulated Motion and Deformable Objects, AMDO, 2006

  13. Wu B, Nevatia R. Simultaneous object detection and segmentation by boosting local shape feature based classifier. In: CVPR, 2007

  14. Tuzel O, Porikli F, Meer P. Human detection via classification on riemannian manifolds. In: Proc. CVPR, 2007. 1–8

  15. Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Trans Patt Anal Mach Intell, 2002, 2(4): 509–522

    Article  Google Scholar 

  16. Seemann E, Leibe B, Mikolajczyk K, et al. An evaluation of local shape-based features for pedestrian detection. In: Proc. BMVC, 2005

  17. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Patt Analy Mach Intell, 2000, 22: 888–905

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China

    Qiang Chen, EnLiang Zheng & YunCai Liu

Authors
  1. Qiang Chen
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  2. EnLiang Zheng
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  3. YunCai Liu
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Corresponding author

Correspondence to Qiang Chen.

Additional information

Supported by the National Basic Research Program of China (Grant No. 2006CB303103), and Key Program of the National Natural Science Foundation of China (Grant No. 60833009)

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Chen, Q., Zheng, E. & Liu, Y. Pose estimation based on human detection and segmentation. Sci. China Ser. F-Inf. Sci. 52, 244–251 (2009). https://doi.org/10.1007/s11432-009-0031-y

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  • DOI: https://doi.org/10.1007/s11432-009-0031-y

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