Sports video classification using HMMS
Xavier Gibert, Huiping Li, David Doermann · 2003
In this paper we address the problem of sports video classification using hidden Markov models (HMMs). For each sports genre, we construct two HMMs representing motion and color features respectively. The observation sequences generated from the principal motion direction and the principal color of each frame are fed to a motion and a color HMM respectively. The outputs are integrated to make a final decision. We tested our scheme on 220 minutes of sports video with four genre types: ice hockey, basketball, football, and soccer, and achieved an overall classification accuracy of 93%.