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Regularity Guaranteed Human Pose Correction

  • Wei ShenEmail author
  • Rui Lei
  • Dan Zeng
  • Zhijiang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

Benefited from the advantages provided by depth sensors, 3D human pose estimation has become feasible. However, the current estimation systems usually yield poor results due to severe occlusion and sensor noise in depth data. In this paper, we focus on a post-process step, pose correction, which takes the initial estimated poses as the input and deliver more reliable results. Although the regression based correction approach [1] has shown its effectiveness in decreasing the estimated errors, it cannot guarantee the regularity of corrected poses. To address this issue, we formulate pose correction as an optimization problem, which combines the output of the regression model with a pose prior model learned on a pre-captured motion data set. By considering the complexity and the geometric property of the pose data, the pose prior is estimated by von Mises-Fisher distributions in subspaces following divide-and-conquer strategies. By introducing the pose prior into our optimization framework, the regularity of the corrected poses is guaranteed. The experimental results on a challenging data set demonstrate that the proposed pose correction approach not only improves the accuracy, but also outputs more regular poses, compared to the-state-of-the-art.

Keywords

Depth Image Temporal Constraint Prior Model Golf Swing Motion Capture Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 61303095, in part by Research Fund for the Doctoral Program of Higher Education of China under Grant 20133108120017, in part by Innovation Program of Shanghai Municipal Education Commission under Grant 14YZ018, in part by Innovation Program of Shanghai University under Grant SDCX2013012 and in part by Cultivation Fund for the Young Faculty of Higher Education of Shanghai under Grant ZZSD13005. We thank Microsoft Corporation for providing the skeleton data set used in our experiments.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Communication and Information EngineeringShanghai UniversityShanghaiPeople’s Republic of China

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