Player Information Extraction for Semantic Annotation in Golf Videos

In sports videos, text provides semantic information about the game such as scores and players. This paper provides an accurate extraction method of the player information in golf. First, a new detection method of the key captions containing the player information is presented. Since the location of the key captions containing the player information is not fixed during a game in golf, we use a color pattern of captions and its temporal repetition property instead of the location property to decide the key captions. Second, a dual binarization method is presented to segment texts with different color polarities (i.e. dark and bright texts) easily from the background in the key captions. Finally, the binarization results are recognized by OCR and converted to plain texts. The player is recognized by comparing the plain texts with the pre-reserved player name database. Experiments on a large database show that our method can extract the player information efficiently in golf videos.