SVM-based soccer video summarization system

In this paper, we propose a system for soccer video summarization using support vector machine (SVM). The proposed system initially segments the whole video stream into small video shots. Then, the system applies support vector machine (SVM) algorithm for emphasizing important segments with logo appearance with addition to detecting the caption region providing information about the score of the game. Subsequently, the system uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor filter for detecting goal net. Finally the system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. The proposed system greatly reduces workload and enhances the accuracy of summarizing soccer video matches with reference to both recall and precision performance measurement criteria.