#75: Semantic event detection and classification in cricket video sequence


M. H. Kolekar, K. Palaniappan, and S. Sengupta

IEEE Indian Conference on Computer Vision, Graphics and Image Processing, pgs. 382--389, 2008

fmv, features, fusion, visual events, data mining, cbir

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Abstract

In this paper, we present a novel hierarchical frame- work and effective algorithms for cricket event detection and classification. The proposed scheme performs a top- down video event detection and classification using hierar- chical 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 clas- sify 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 fre- quently occurring classes batsman, bowler/fielder, umpire using jersey color feature. At level-5b, we classify crowd segment into the two frequently occurring classes specta- tor and players’ gathering using color feature. We show promising results, with correctly classified cricket events, enabling structural and temporal analysis, such as high- light extraction, and video skimming.