The problem of automatic excitement detection in baseball videos is considered and applied for highlight generation. This paper focuses on detecting exciting events in video using complementary information from the audio and video domains. First, a new measure for non-stationarity which is extremely effective in separating background from speech is proposed. This new feature is employed in an unsupervised GMM-based segmentation algorithm that identifies the sports commentators speech within the crowd background. Thereafter, the "level-of-excitement" is measured using features such as pitch, F1-F3 center frequencies, and spectral center of gravity extracted from the commentators speech. Our experiments using actual baseball videos show that these features are well correlated with human assessment of excitability. Furthermore, slow-motion replay and baseball pitching-scenes from the video are also detected to estimate scene end-points. Finally, audio/video information is fused to rank-order scenes by "excitability" in order to generate highlights of user-defined time-lengths. The techniques described in this paper are generic and applicable to a variety of topic and video/acoustic domains.
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