HMM model selection issues for soccer video

M. Baillie, J.M. Jose, C.J. van Rijsbergen

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

There has been a concerted effort from the Video Retrieval community to develop tools that automate the annotation process of Sports video. In this paper, we provide an in-depth investigation into three Hidden Markov Model (HMM) selection approaches. Where HMM, a popular indexing framework, is often applied in a ad hoc manner. We investigate what effect, if any, poor HMM selection can have on future indexing performance when classifying specific audio content. Audio is a rich source of information that can provide an effective alternative to high dimensional visual or motion based features. As a case study, we also illustrate how a superior HMM framework optimised using a Bayesian HMM selection strategy, can both segment and then classify Soccer video, yielding promising results.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
PublisherSpringer
ISBN (Print)0302-9743
DOIs
Publication statusPublished - 2004

Keywords

  • information retrieval
  • video retrieval
  • learning objects
  • hidden Markov model
  • classification
  • sport

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    Baillie, M., Jose, J. M., & van Rijsbergen, C. J. (2004). HMM model selection issues for soccer video. In Lecture Notes in Computer Science Springer. https://doi.org/10.1007/b98923