| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 95 === Watching sports videos has always been an important and popular recreation. The audiences nowadays can enjoy watching the sports games at home with their high-quality audio-visual facilities and even record the videos by using digital video recorders. When the audiences choose to record the video for time-shift purposes, they may not be interested in watching the whole game but the video highlights only. In addition, the highlight parts in current sportscasts are always followed by slow-motion replays. A transition effect is usually inserted between the normal frame and the replaying frame to inform the audiences of the replay. Therefore, the appearance of a transition effect has a direct linkage to the video highlight. In this research, we propose to detect transition effects for baseball videos highlight extraction. In order to reduce the computational cost of hardware, the proposed method processes MPEG compressed bit-streams directly. We make use of the color information of MPEG streams and the motion information including motion vectors and the macro-block types in frames. Then we analyze to determine whether the transition effects occur by the characteristics of transition effects. Next, we use the detected transition effects to train a template, which will be used for matching in the remaining parts of video. Furthermore, we classify the replay segments so that the user can choose the video segment that he or she really likes to watch. Since the users will be more interested in watching the normal scenes of highlights, we trace back to find out the pitching view as the starting point of a highlight. Experimental results show the feasibility of the highlight extraction system.
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