Development of realistic applications based on explicit event detection in broadcasting baseball videos

This paper presents a framework that explicitly detects events in broadcasting baseball videos and facilitates the development of various extended applications. Three phases are included: reliable shot classification, explicit event detection, and elaborate applications. In the shot classification stage, color and geometric information are utilized to classify shots into several canonical views. To explicitly detect semantic events, rule-based decision and model-based decision methods are developed. We emphasize that this system efficiently and exactly identifies what happened in baseball games rather than roughly finding some interesting parts. Based on explicit event detection, many accurate and practical applications such as box score generation and game summarization can be built. The evaluation results show the effectiveness of the proposed methods and demonstrate some insights about bridging semantic gaps for sports videos.