Audiovisual Integration for Racquet Sports Video Retrieval.

This paper presents a new audiovisual integration scheme for racquet sports video structure indexing and highlight generating. Instead of using low-level features, the method is built upon the combination of visual and audio features. With respect to prior information about this kind of video content and editing rules, visual features based on dominant color and motion attention model are applied to classify shots into two classes: global view shots and non-global view shots. The classification algorithm is independent of predefined court color, and much robust to lighting conditions. Afterwards, among shots important auditory features including both ball hitting and applause are detected for identifying interesting events with strong semantic meaning, such as missed serves, aces, rallies and replays in tennis video. Finally, a reasonable model is built to rank rally events by excitement. The results showed the scheme could effectively identify typical scenes for retrieving highlights. [ABSTRACT FROM AUTHOR]