Automatic Detection And Annotation Of Highlights Within Various Types Of Sports Clips

Sports video is a hot research topic for its wide viewer-ship and enormous application potential in recent years. This paper gives a review of relative research work including low-level feature extraction, mid-level keyword generation, high-level semantics inference, relative applications and system prototypes, and finally indicates potential trend.Automatic detection and annotation of highlights within sports video is a complicated systems engineering. In this paper, we propose to use a visual object detection algorithm to find local in addition to an audio object in the audio track for sports highlights extraction.This project exchange this information with GMM2 towards building a system that can adapt the transmission according to the availability of bandwidth and the importance of the content being viewed. Here I divide the project into 2 parts, part manually part and automatically part. If, for sports program TV broadcasts, the audio amplitude is assumed to primarily reflect the noise level exhibited by the commentator, then this vocal reaction to the significance of unfolding events may be used as a basis for summarization by relying on the exhilaration, or otherwise, expressed by the commentator/spectators, individual clips of the program, may be ranked according to their relative significance. A summary may then be produced by amalgamating any number of these clips corresponding to selected audio peaks.This analysis framework is proposed based on adequate comparison of existing multi-model information fusion methods and the discovery of periodic characteristic of racquet sports video. It is a general sports video content analysis method based on audio/visual middle level features, domain rules, context, and highlights ranking. Its advantages include simplicity, intuitiveness, generality, context-sensitive and affectivity. The details are as follows:1) Use audio define and analysis the highlights2) Before system executing, most of the job need to split manually, detect and analysis the highlights according to the audio, it is the precondition of this system.3) The system has achieved a series of function: split the audio from the video, can convert the format of the audio to corresponding format, automatic define and detect the highlights of the video.4) Use these methods, one video come in to this system will be split 3 level segments.5) GMM2 is an automatic detection system, according to the availability of bandwidth and the importance of the content being viewed.This system integrates most advanced dot net component technique, spatial database engine technique and C++ technique, WAV file. This system will be extended to a large comprehensive system containing other methods. So it will improve our user's efficiency, planning and management; greatly improve the user of the TV station's efficiency.