Multimodal automatic indexing for broadcast soccer video

Abstract

In this paper, we propose a novel method for estimating major soccer scenes from cameraworks and players trajectories based on probabilistic inference, and annotating scene indexes to broadcast soccer videos automatically. In our method, we define relations between cameraworks and scenes, and between players trajectories and scenes by conditional probabilities. Moreover defining temporal relations of scenes by transition probabilities, we represent those relations as dynamic bayesian networks (DBNs). And those probabilities are evaluated by learning parameters of the networks. After extracting the cameraworks and the players trajectories, we compute the posterior probability distribution of scenes, and give the computed results to the soccer video as the scene index. Finally, we discuss the extendibility of the proposal indexing technique in the case of adding ball trajectories and audios. © Springer-Verlag Berlin Heidelberg 2005.