| Summary: | 博士 === 國立交通大學 === 資訊科學與工程研究所 === 102 === 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 dissertation, a novel framework will be proposed to tackle challenges of sports 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.
As to semantic event extraction, most of existing researches focus on analyzing audio-visual features of video content as resource knowledge. However, schemes relying on video content encounter a challenge called semantic gap, which represents the distance between lower level video features and higher level semantic events. Although the multimodal fusion scheme that conducts webcast text as external knowledge to bridge the semantic gap has been proposed recently, extracting semantic events from sports webcast text and annotating semantic events in sports videos are still challenging tasks. In this dissertation, we will address the challenges in the multimodal fusion scheme. Then, we will propose two methods to overcome the challenges.
As to slow motion replay detection, many methods have been proposed, and they are classified into two categories. One assumes that a replay is sandwiched by a pair of visually similar special digital video effects, but the assumption is not always true in basketball videos. The other analyzes replay features to distinguish replay segments from non-replay segments. The results are not satisfactory since some features (e.g. dominant color of sports field) are not applicable for basketball. Most replay detectors focus on soccer videos. In this dissertation, we will propose a novel idea to detect slow motion replays in basketball videos.
The feasibility and effectiveness of all the above proposed methods have been demonstrated in experiments. It is expected that the proposed sports video analysis framework can be extended to other sports.
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