Hierarchical structure analysis of sport videos using HMMS

Abstract

This paper focuses on the use of hidden Markov models (HMMs) for structure analysis of sport videos. The video structure parsing relies on the analysis of the temporal interleaving of video shots, with respect to a priori information about video content and editing rules. The basic temporal unit is the video shot and visual features are used to characterize its type of view. Our approach is validated in the particular domain of tennis videos. As a result, typical tennis scenes are identified. In addition, each shot is assigned to a level in the hierarchy described in terms of point, game and set.