Mining Information of Attack-Defense Status from Soccer Video Based on Scene Analysis

Video content is always huge by itself with abundant information. Extracting explicit semantic information has been extensively investigated such as object detection, structure analysis and event detection. However, little work has been devoted on the problem of discovering global or inexplicit information from the huge video stream. As an implementation in this topic, this paper proposes a solution to mining the statistical global attack-defense status information from soccer video by scene analysis. Semantic scene information of play field detection, view classification, midline detection and global motion are extracted as the mid level information, and then they are fed into the finite state machine based status mining model to generate the statistical results, which will be of much usefulness for users. Experimental results reveal the feasibility of the method and more research work on the topic of discovering high-level inexplicit information from video are expected.