Extract highlights from baseball game video with hidden Markov models

We describe a statistical method to detect highlights in a baseball game video. The input video is first segmented into scene shots, within which the camera motion is continuous. Our approach is based on the observations that (1) most highlights in baseball games are composed of certain types of scene shots and (2) those scene shots exhibit special transition context in time. To exploit those two observations, we first build statistical models for each type of scene shots with products of histograms, and then for each type of highlight a hidden Markov model is learned to represent the context of transition in the time domain. A probabilistic model can be obtained by combining the two, which is used for highlight detection and classification. Satisfactory results have been achieved on initial experimental results.