Semantic labeling of soccer video

As traditional shot segmentation may not produce video segments that possess one-to-one correspondence to semantic views, we present an integrated segmentation and classification approach to label soccer video into semantic units in this paper. In our system, each P frame is divided to a 6 × 4 blocks with color and motion features extracted on both block and frame levels. First, a threshold is used to divide the video stream into relatively static parts and active parts. Then every active part is segmented into sub-parts according to 4 view types and the motion features are used to classify segments with support vector machines. Finally, static parts are merged with classified active sub-parts to form labeled segments. Four 10-minute test clips from the World Cup 2002 are used to evaluate our system resulting in a promising classification rate of 79.8%.