Marker-based based motion capture is the prevalent technique for estimating human motion. A common problem with the approach is the occlusion and mis-labeling of the markers; typically the data requires tedious manual cleaning in post processing. We present a constrained extended Kalman filter method that estimates full body human motion in real time and handles missing and mis-labeled markers. The approach is validated on two datasets and is shown to produce comparable results to using manually cleaned data. The constrained estimator ensures realistic human joint trajectories that satisfy kinematic limits.


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    Title :

    Real-Time Unlabeled Marker Pose Estimation via Constrained Extended Kalman Filter


    Additional title:

    Springer Proceedings in Advanced Robotics


    Contributors:

    Conference:

    International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018



    Publication date :

    2020-01-23


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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