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Filtered Pose Graph for Efficient Kinect Pose Reconstruction

Pierre Plantard, Hubert P. H. Shum and Franck Multon
Multimedia Tools and Applications (MTAP), 2017

 Impact Factor: 3.6 Citation: 44#

Filtered Pose Graph for Efficient Kinect Pose Reconstruction
# According to Google Scholar 2023"


Being marker-free and calibration free, Microsoft Kinect is nowadays widely used in many motion-based applications, such as user training for complex industrial tasks and ergonomics pose evaluation. The major problem of Kinect is the placement requirement to obtain accurate poses, as well as its weakness against occlusions. To improve the robustness of Kinect in interactive motion-based applications, real-time data-driven pose reconstruction has been proposed. The idea is to utilize a database of accurately captured human poses as a prior to optimize the Kinect recognized ones, in order to estimate the true poses performed by the user. The key research problem is to identify the most relevant poses in the database for accurate and efficient reconstruction. In this paper, we propose a new pose reconstruction method based on modelling the pose database with a structure called Filtered Pose Graph, which indicates the intrinsic correspondence between poses. Such a graph not only speeds up the database poses selection process, but also improves the relevance of the selected poses for higher quality reconstruction. We apply the proposed method in a challenging environment of industrial context that involves sub-optimal Kinect placement and a large amount of occlusion. Experimental results show that our real-time system reconstructs Kinect poses more accurately than existing methods.





 author={Plantard, Pierre and Shum, Hubert P. H. and Multon, Franck},
 journal={Multimedia Tools and Applications},
 title={Filtered Pose Graph for Efficient Kinect Pose Reconstruction},
 Address={Berlin, Heidelberg},


AU  - Plantard, Pierre
AU  - Shum, Hubert P. H.
AU  - Multon, Franck
T2  - Multimedia Tools and Applications
TI  - Filtered Pose Graph for Efficient Kinect Pose Reconstruction
PY  - 2017
VL  - 76
IS  - 3
SP  - 4291
EP  - 4312
DO  - 10.1007/s11042-016-3546-4
SN  - 1573-7721
PB  - Springer-Verlag
ER  - 

Plain Text

Pierre Plantard, Hubert P. H. Shum and Franck Multon, "Filtered Pose Graph for Efficient Kinect Pose Reconstruction," Multimedia Tools and Applications, vol. 76, no. 3, pp. 4291-4312, Springer-Verlag, 2017.

Supporting Grants

Similar Research

Zhiguang Liu, Liuyang Zhou, Howard Leung and Hubert P. H. Shum, "Kinect Posture Reconstruction Based on a Local Mixture of Gaussian Process Models", IEEE Transactions on Visualization and Computer Graphics (TVCG), 2016
Liuyang Zhou, Zhiguang Liu, Howard Leung and Hubert P. H. Shum, "Posture Reconstruction Using Kinect with a Probabilistic Model", Proceedings of the 2014 ACM Symposium on Virtual Reality Software and Technology (VRST), 2014
Hubert P. H. Shum, Edmond S. L. Ho, Yang Jiang and Shu Takagi, "Real-Time Posture Reconstruction for Microsoft Kinect", IEEE Transactions on Cybernetics (TCyb), 2013
Pierre Plantard, Hubert P. H. Shum and Franck Multon, "Ergonomics Measurements using Kinect with a Pose Correction Framework", Proceedings of the 2016 International Digital Human Modeling Symposium (DHM), 2016
Pierre Plantard, Hubert P. H. Shum and Franck Multon, "Usability of Corrected Kinect Measurement for Ergonomic Evaluation in Constrained Environment", International Journal Human Factors Modelling and Simulation (IJHFMS), 2017
Pierre Plantard, Antoine Muller, Charles Pontonnier, Georges Dumont, Hubert P. H. Shum and Franck Multon, "Inverse Dynamics Based on Occlusion-Resistant Kinect Data: Is It Usable for Ergonomics?", International Journal of Industrial Ergonomics (IJIE), 2017



Last updated on 17 February 2024
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