Picture of scraped petri dish

Scrape below the surface of Strathprints...

Explore world class Open Access research by researchers at the University of Strathclyde, a leading technological university.

Explore

HMM model selection issues for soccer video

Baillie, M. and Jose, J.M. and van Rijsbergen, C.J. (2004) HMM model selection issues for soccer video. In: Lecture Notes in Computer Science. Springer. ISBN 0302-9743

Full text not available in this repository. (Request a copy from the Strathclyde author)

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.