| Summary: | 博士 === 義守大學 === 資訊工程學系博士班 === 97 === In the past decade, because the information amounts of broadcast sport videos have increased explosively, in order to manage the large amount of data, automatic high-level semantic analysis for sport videos has received intensive attention. This dissertation presents an effective and efficient event detection system for broadcast baseball videos. It integrates mid-level cues including scoreboard information and shot transition patterns into event classification rules. First, a simple scoreboard detection and recognition scheme is developed to extract the game status from videos. Then, a shot transition classifier is designed to obtain the shot transition patterns, which contains several novel schemes including adaptive playfield segmentation, pitch shot and field shot detection, as well as infield/outfield classification. The extracted mid-level cues are used to develop an event classifier based on a Bayesian Belief Network. The network is with low complexity because the number of these cues used is small, which not only improves the performance of the event classifier but also reduces system complexity as well as training efforts. Using the inference results of the network, we further derive a set of classification rules to identify baseball events. The sets of rules are stored in a look-up table such that the classification is only a simple table look-up operation. The proposed approach is very simple and computationally efficient. More importantly, the simulation results indicate that it identifies ten significant baseball events with 95% of precision rate and 92% of recall rate, which is very promising.
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