A novel ball detection framework for real soccer video

Abstract

Despite a lot of research efforts in sports video analysis, soccer video indexing remains a challenging task due to the lack of structure in a soccer game that could help in structure analysis. In particular, little work was done in detecting and tracking the ball whose trajectory could play a crucial role for detecting key events. We propose a novel framework for accurately detecting the ball for broadcast soccer video. Our framework combines both direct and indirect insights to identify the ball rather than conventional simple template matching methods. It has three key components. First we infer the ball size range from the player size. Next non-ball objects are removed to reduce the possible ball candidates. Last but not least, a Kalman filer-based procedure mines candidate trajectories in candidate feature images. Then, a procedure selects the reliable ball trajectories from them. The experimental results on two 1000-frame sequences confirm that the proposed framework is very effective and obtain a better result than existing methods.