Use of Context in Automatic Annotation of Sports Videos

  • Ilias Kolonias
  • William Christmas
  • Josef Kittler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

The interpretation by a human of a scene in video material is heavily influenced by the context of the scene. As a result, researchers have recently made more use of context in the automation of scene understanding. In the case of a sports video, useful additional context is provided by formal sets of rules of the sport, which can be directly applied to the understanding task. Most work to date has used the context at a single level. However we claim that, by using a multilevel contextual model, erroneous decisions made at a lower can be avoided by the influence of the higher levels. In this work, we explore the use of a multilevel contextual model in understanding tennis videos. We use Hidden Markov models as a framework to incorporate the results of the scene analysis into the contextual model. Preliminary results have shown that the proposed system can successfully recover from errors at the lower levels.

Keywords

Hide Markov Model Contextual Model Optical Character Recognition Viterbi Algorithm Automatic Annotation 
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 2004

Authors and Affiliations

  • Ilias Kolonias
    • 1
  • William Christmas
    • 1
  • Josef Kittler
    • 1
  1. 1.Center for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

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