Our goal is to propose a flexible and efficient solution to generate summary for sports video data. To this end, we focus on incorporating the standard audio-visual features (MPEG-7 descriptors) with domain-specific information in a common framework, and thus locate and extract interesting events. Currently, our test dataset are soccer videos.   Figure 1: Framework of video summarization system   For soccer video:   Feature extraction: two features Ration of grass color region that is made from MPEG-7 Scalable color descriptors. Edge histogram made from MPEG-7 Scalable color descriptors.   Shot detection: three categories of shots: global view, medium view and close-up view.       Scene construction: two kinds of events: "goal", and "foul and missing".   Advantages The system is flexible and easy to extend to other domain. It has high accuracy, the achieved results are 90.7% of of shot detection, 67%, and 90% of goal, and "foul and missing shot" event detection, respectively.     Nguyen Ngoc Thanh, Truong Cong Thang, Beet Nara Bea, Yong Man Ro, "Video Abstraction Generation Supervised by User Preference", SPIE 2003 Nguyen Ngoc Thanh, Truong Cong Thang, Tae Meon Bae, Yong Man Ro, "Soccer Video Summarization System Based on Hidden Markov Model with Multiple MPEG-7 Descriptors", Submited to CISST 2003   Click here to see examples of soccer video summary.