Arousal content representation of sports videos using dynamic prediction hidden Markov models

This paper develops dynamic prediction hidden Markov models for arousal time curve estimation in sports videos. The method determines the arousal time curve by selecting a state sequence that maximizes the joint probability density function between the states and the arousal time curve. We derive the parameters using the expected maximization algorithm. Experiments were performed on several types of sports videos. Test measures include squared residual error and criteria derived from psychology. The experimental results show that the novel method performed better in estimating the arousal time curve than state of the art linear regression methods on most of the tested sports videos.