Structural Analysis And Summarization Of Soccer Video

As digital sports video data become more and more pervasive, finding the clips of highlights manually in large amount of video data is a boring and tedious task, automatic highlight detection system is particularly demanding.In this paper, it presents an automatic and effective framework for highlight events detection based on cinematic and object features to get video summarization. The proposed framework includes 4 parts which are shot boundary detection, shot classification, slow-motion replay shot detection and video summarization. The shot boundary detection employs the multi-filter structure that combines traditional pixel level comparison and histogram comparison with object segmentation and object tracking for the particularity in soccer video. After the shot boundary detection, there are two different methods for the shot classification. The shots can be classified by the soccer field region, and the soccer field can be divided into 9 parts. The shots can also be classified by the background of the soccer video which the domain color is the tone of green. The soccer shots are classified into three classes which are long shots, medium shots, out of field or close-up shots by using this feature. It presents gold section and finding the max ungreen pixel rectangle algorithms to partition long shot and medium shot in shot classification. Then, the highlight event uses the slow-motion replay and association rule to locate the highlight shot and the new template to identify the highlight event and extract summarization on the basis of goal event.The system can output two types of summaries which are all slow-motion segments in a game and all highlight events in a game. The first type of summaries is based on cinematic and low-level features only for speedy processing, while the summaries of the second type contain higher-level semantics. It is efficient in the sense that there is no need to compute object-based features when cinematic and low-level features are sufficient for the detection of certain events. It is effective in the sense that the framework can also employ object-based features when needs to increase accuracy. The summarization that is extracted from soccer video can be used to implement content-based video retrieval.