Real-time segmentation methods for monocular soccer videos

The analysis of video records of soccer games is a topic of extensive and challenging research in image sequence processing, particularly that of player and field tracking. In this paper, we propose a new approach for detecting players and field lines in monocular TV video data that involves determining the convex field area and grass colors. This is carried out by considering contextual knowledge, together with a new method for color segmentation that selects polyhedrons in a frame-wise manner within the RGB cube. In summary, for every input image, a binary mask of the field area and a background mask are determined. Both mask creation algorithms achieve per pixel F1 scores of about 98% within our representative test set and are real-time capable. Applications like line detection and player tracking are presented.