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Robust Player Gesture Spotting and Recognition in Low-Resolution Sports Video

  • Myung-Cheol Roh
  • Bill Christmas
  • Joseph Kittler
  • Seong-Whan Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)

Abstract

The determination of the player’s gestures and actions in sports video is a key task in automating the analysis of the video material at a high level. In many sports views, the camera covers a large part of the sports arena, so that the resolution of player’s region is low. This makes the determination of the player’s gestures and actions a challenging task, especially if there is large camera motion. To overcome these problems, we propose a method based on curvature scale space templates of the player’s silhouette. The use of curvature scale space makes the method robust to noise and our method is robust to significant shape corruption of a part of player’s silhouette. We also propose a new recognition method which is robust to noisy sequences of data and needs only a small amount of training data.

Keywords

Input Image Gesture Recognition Sport Video Silhouette Image Foreground Image 
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 2006

Authors and Affiliations

  • Myung-Cheol Roh
    • 1
  • Bill Christmas
    • 2
  • Joseph Kittler
    • 2
  • Seong-Whan Lee
    • 1
  1. 1.Center for Artificial Vision ResearchKorea Univ.SeoulKorea
  2. 2.Center for Vision, Speech, and Signal ProcessingUniv. of SurreyGuildfordUK

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