Fusion of audio and motion information on HMM-based highlight extraction for baseball games

Abstract

This paper aims to extract baseball game highlights based on audio-motion integrated cues. In order to better describe different audio and motion characteristics in baseball game highlights, we propose a novel representation method based on likelihood models. The proposed likelihood models measure the "likeliness" of low-level audio features and motion features to a set of predefined audio types and motion categories, respectively. Our experiments show that using the proposed likelihood representation is more robust than using low-level audio/motion features to extract the highlight. With the proposed likelihood models, we then construct an integrated feature representation by symmetrically fusing the audio and motion likelihood models. Finally, we employ a hidden Markov model (HMM) to model and detect the transition of the integrated representation for highlight segments. A series of experiments have been conducted on a 12-h video database to demonstrate the effectiveness of our proposed method and show that the proposed framework achieves promising results over a variety of baseball game sequences.