Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data

Jürgen Bernard, Eduard Dobermann, Anna Vögele, Björn Krüger, Jörn Kohlhammer, and D. Fellner
In proceedings of Visualization and Data Analysis (VDA 2017), Jan. 2017
 

Abstract

The characterization and abstraction of large multivariate time series data often poses challenges with respect to effectiveness or efficiency. Using the example of human motion capture data challenges exist in creating compact solutions that still reflect semantics and kinematics in a meaningful way. We present a visual-interactive approach for the semi-supervised labeling of human motion capture data. Users are enabled to assign labels to the data which can subsequently be used to represent the multivariate time series as sequences of motion classes. The approach combines multiple views supporting the user in the visual-interactive labeling process. Visual guidance concepts further ease the labeling process by propagating the results of algorithmic models. The abstraction of motion capture data to sequences of event intervals allows overview and detail-on-demand visualizations even for large and heterogeneous data collections. The guided selection of candidate data for the extension and improvement of the labeling closes the feedback loop of the semisupervised workflow. We demonstrate the effectiveness and the efficiency of the approach in two usage scenarios, taking visual-interactive learning and human motion synthesis as examples.

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Bibtex

@INPROCEEDINGS{bernard2017a,
     author = {Bernard, J{\"u}rgen and Dobermann, Eduard and V{\"o}gele, Anna and Kr{\"u}ger, Bj{\"o}rn and Kohlhammer, J{\"o}rn
               and Fellner, D.},
      title = {Visual-Interactive Semi-Supervised Labeling of Human Motion  Capture Data},
  booktitle = {Visualization and Data Analysis (VDA 2017)},
       year = {2017},
      month = jan,
   abstract = {The characterization and abstraction of large multivariate time series data often poses challenges
               with respect to effectiveness or efficiency. Using the example of human motion capture data
               challenges exist in creating compact solutions that still reflect semantics and kinematics in a
               meaningful way. We present a visual-interactive approach for the semi-supervised labeling of human
               motion capture data. Users are enabled to assign labels to the data which can subsequently be used
               to represent the multivariate time series as sequences of motion classes. The approach combines
               multiple views supporting the user in the visual-interactive labeling process. Visual guidance
               concepts further ease the labeling process by propagating the results of algorithmic models. The
               abstraction of motion capture data to sequences of event intervals allows overview and
               detail-on-demand visualizations even for large and heterogeneous data collections.
               The guided selection of candidate data for the extension and improvement of the labeling closes the
               feedback loop of the semisupervised workflow. We demonstrate the effectiveness and the efficiency of
               the approach in two usage scenarios, taking visual-interactive learning and human motion synthesis
               as examples.}
}