Cairo University, Faculty of Computers & Information Information Technology Department 5 Ahmed Zewal St., Orman, Giza, office box# 12613 (email)
This thesis presents an automatic soccer video summarization system using machine learning (ML) techniques. The proposed system is composed of ve phases. Namely; in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing
phase, it applies two types of classi cation (shot type classi cation and play / break classification) to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the proposed system applies two machine learning algorithms, namely; support vector machine (SVM) and arti cial neural network (ANN), for emphasizing important segments with championship logo appearance. Also, in the excitement event detection phase, the proposed system uses both machine learning algorithms for detecting the scoreboard which contain an information about the score of the game. The proposed system also uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor lter for detecting goal net. Finally, in the event detection and summarization phase, the proposed system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. The event detection and summarization has attained recall 94% and precision 97.3% for soccer match videos from ve international soccer championships.