碩士 / 國立交通大學 / 資訊科學與工程研究所 / 98 / Despite a lot of research efforts in baseball video processing in recent years, little work has been done in analyzing the detailed semantic baseball event detection. This thesis presents an effective and efficient baseball event classification system for broadcast baseball videos. Utilizing the strictly-defined specifications of the baseball field and the regularity of shot transition, the system recognizes highlight in video clips and identifies what semantic baseball event of the baseball clips is currently proceeding. The semantic exploring system is proposed to achieve the objective. First, a video is segmented into several highlights starting with a PC (Pitcher and Catcher) shot and ending up with a close-up or some specific shots. Before every baseball event classifier is designed, several novel schemes including some specific features such as soil percentage and objects extraction such as first base are applied. The extracted midlevel cues are used to develop baseball event classifiers based on an HMM (Hidden Markov model). Due to specific features detection the proposed method not only improves the accuracy of the highlight classifier but also supports variety types of the baseball events. The proposed approach is very efficient. More importantly, the simulation results show that the classification of twelve significant baseball highlights is very promising.
| Identifer | oai:union.ndltd.org:TW/098NCTU5394052 |
| Date | January 2010 |
| Creators | Tsai, Wei-Chin, 蔡維晉 |
| Contributors | Lee, Suh-Yin, 李素瑛 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 49 |
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