NOVEL FRAMEWORK FOR SPORTS VIDEO ANALYSIS: A BASKETBALL CASE STUDY

Abstract

Semantic event and slow motion replay extraction for sports videos have become hot research topics. Most researches analyze every video frame; however, semantic events only appear in frames with scoreboard, whereas replays only appear in frames without scoreboard. Extracting events and replays from unrelated frames causes defects and leads to degradation of performance. In this paper, a novel framework is proposed to tackle challenges of basketball video analysis. In the framework, a scoreboard detector is first provided to divide video frames to two classes, with/without scoreboard. Then, a semantic event extractor is presented to extract semantic events from frames with scoreboard and a slow motion replay extractor is proposed to extract replays from frames without scoreboard. Experimental results show that the proposed framework is practicable for basketball videos. It is expected that the proposed framework can be extended to other sports.