Approaches and Challenges in the Visual-Interactive Comparison of Human Motion Data

Stichwörter: Data Mining, Human Motion Capture Data, Human-Computer Interaction, Information Retrieval, Information Visualization, Machine Learning, Motion Capture Analysis, visual analytics, Visual Comparison

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Bibtex

@INPROCEEDINGS{bernard2017b,
     author = {Bernard, J{\"u}rgen and V{\"o}gele, Anna and Klein, Reinhard and Fellner, D.},
      title = {Approaches and Challenges in the Visual-Interactive Comparison of Human Motion Data},
    journal = {IVAPP},
  booktitle = {Proceedings of the 8th International Conference on Information Visualization Theory and
               Applications, IVAPP 2017},
       year = {2017},
   keywords = {Data Mining, Human Motion Capture Data, Human-Computer Interaction, Information Retrieval,
               Information Visualization, Machine Learning, Motion Capture Analysis, visual analytics, Visual
               Comparison},
   abstract = {Many analysis goals involving human motion capture (MoCap) data require the comparison of motion
               patterns. Pioneer works in visual analytics recently recognized visual comparison as substantial for
               visual-interactive analysis. This work reflects the design space for visual-interactive systems
               facilitating the visual comparison of human MoCap data, and presents a taxonomy comprising three
               primary factors, following the general visual analytics process: algorithmic models, visualizations
               for motion comparison, and back propagation of user feedback. Based on a literature review, relevant
               visual comparison approaches are discussed. We outline remaining challenges and inspiring works on
               MoCap data, information visualization, and visual analytics.}
}