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Analyzing Diving: A Dataset for Judging Action Quality

  • Kamil Wnuk
  • Stefano Soatto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

This work presents a unique new dataset and objectives for action analysis. The data presents 3 key challenges: tracking, classification, and judging action quality. The last of these, to our knowledge, has not yet been attempted in the vision literature as applied to sports where technique is scored.

This work performs an initial analysis of the dataset with classification experiments, confirming that temporal information is more useful than holistic bag-of-features style analysis in distinguishing dives. Our investigation lays a groundwork of effective tools for working with this type of sports data for future investigations into judging the quality of actions.

Keywords

Background Subtraction Foreground Object Motion Blur Human Action Recognition Background Subtraction Method 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kamil Wnuk
    • 1
  • Stefano Soatto
    • 1
  1. 1.University of CaliforniaLos AngelesUSA

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