Tennis Video Retrieval Based On Audio And Video Integration

This paper uses tennis video as research object, detects the exciting events occurred in the tennis video, such as ACE,Net-approach and so on. And proposes a wonderful tennis video incident detection framework, containing visual semantic analysis, audio semantic analysis, highlights detection based on the fusion of audio-visual information.Visual semantic analysis includes shot classification, player detection, player tracking. Shot classification which directly determines the accuracy of exciting event detection is the foundation of Video analysis. For the current shot classification, combined with the characteristics of tennis game video, shot classification method based on Hough line detection is proposed. The lens is divided into game and non-game camera lens. And then extracts the location of players in the game lens by the frame difference, and accomplish player tracking using Camshift algorithm.Audio semantic analysis includes frame-level audio features extraction and audio clip recognition. This paper designs an algorithm to extract average short time energy, short time zero-crossing rate, MFCC and difference MFCC. And then an audio classifier based on continuous hidden Markov model is realized which divides audio information in tennis game into five classes:shots, cheers, excited commentary, normal commentary and background noise.Finally, ACE ball, Base-line Rally and Net-approach can be abstracted according to the length of the lens, position of player, player movement, shots and cheers.In summary, this paper proposes an algorithm to automatically analyze and extract tennis game highlights scene based on the fusion of audio-visual features. And a prototype system, using Intel OpenCV Library, for automatic tennis video semantic analysis by Visual C++6.0 and matlab 7.0 is implemented. Experiments have demonstrated that all these methods are effective.