Semantic Event Detection and Classification in Cricket Video Sequence

In this paper, we present a novel hierarchical framework and effective algorithms for cricket event detection and classification. The proposed scheme performs a topdown video event detection and classification using hierarchical tree which avoids shot detection and clustering. In the hierarchy, at level-1, we use audio features, to extract excitement clips from the cricket video. At level-2, we classify excitement clips into real-time and replay segments. At level-3, we classify these segments into field view and non-field view based on dominant grass color ratio. At level-4a, we classify field view into pitch-view, long-view, and boundary view using motion-mask. At level-4b, we classify non-field view into close-up and crowd using edge density feature. At level-5a, we classify close-ups into the three frequently occurring classes batsman, bowler/fielder, umpire using jersey color feature. At level-5b, we classify crowd segment into the two frequently occurring classes spectator and playerspsila gathering using color feature. We show promising results, with correctly classified cricket events, enabling structural and temporal analysis, such as highlight extraction, and video skimming.