Generation of personalized abstract of sports video
Video abstraction is defined as creating a shorter video clip from an original video stream. In this paper, we propose a method of generating a personalized abstract of broadcasted sports video. We first detect significant events from the video stream by matching with gamestats in which highlights of the game are described. Textual information in an overlay appearing on an image frame is recognized for this matching. Then, we select highlight shots from these detected events, reflecting on personal preferences. Finally, we connect each shot augmented with related audio and text in temporal order. From experimental results, we verified that an hourlength video can be compressed into a minute-length personalized abstract.