Enhanced Sports Video Shot Boundary Detection Based on Middle Level Features and a Unified Model

The performance of shot boundary detection algorithms using low level features can hardly fulfill the requirement of automatic sports video analysis, which is a promising module in personal video recorders. Two kinds of middle level features are proposed in this paper to effectively enhance the shot boundary detection. One kind of the features is extracted from the projection of the dominant color mask; the other kind is extracted from the reliable block-based motion vectors. These novel features, together with the region color histograms feature, are integrated into a unified model which employs support vector machines to detect both cuts and gradual transitions. Experiments on diversified soccer video sequences demonstrate that our scheme outperforms the existing algorithms and is quite competent for the targeted applications.