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Title page for etd-0817107-134519


URN etd-0817107-134519 Statistics This thesis had been viewed 2668 times. Download 1010 times.
Author Ching-Lin Chiou
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2006 Semester 2
Degree Master Type of Document Master's Thesis
Language English Page Count 73
Title Using Artificial Intelligent Strategies to Detect Tennis Sport Highlights
Keyword
  • Recall
  • Artificial Intelligence
  • Sport video
  • Highlight
  • Precision
  • Precision
  • Highlight
  • Sport video
  • Artificial Intelligence
  • Recall
  • Abstract In recent years, due to the popularity of sport activities, general masses or sport professionals can utilize various video recording tools to record, produce, pick and fetch useful information or interesting programs. Sport video analyses have attracted attention gradually. Sport video has an inherent structure as defined in rules of the game and field production. In addition, some highlights in the sports game such as homerun in the baseball, shot in the football and ace in the tennis make the sport video more suitable for further investigation than other types of video such as the films and news.
    Tennis sport video is selected as the primary domain due to its content richness and popularity. With an ongoing rapid growth of sport video information, there is an emerging demand for a sophisticated tennis content-based video indexing system. However, current video indexing solutions are still immature and lack of any standard.
    Artificial intelligent strategies are proposed to combine characteristics in the audio and image domain and knowledge to find the highlight. Moreover, broadcasted sports videos generally last several hours with many redundant advertisements and the key segments are not easy to find. The proposed model can be extended to different application domains, such as tennis acrobatics training, exciting news editing, and program preview in the future.
    Based on the experimental results, both the average of precision and recall rates are higher than 89%. This verifies that the proposed model is effective in extracting the highlights of tennis sport.
    Advisor Committee
  • Yo-Ping Huang - advisor
  • Shyi-Ming Chen - co-chair
  • Wei Yen - co-chair
  • Files indicate access worldwide
    Date of Defense 2007-06-11 Date of Submission 2007-08-17


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