Sports event detection using temporal patterns mining and web-casting text
Minh-Son Dao, Noburu Babaguchi · 2008
Event detection is one of the essential tasks by which the performance of sports video content analysis and access becomes more efficient and effective. Among internal information which are extracted from inside raw videos, the temporal information is critical to convey event meaning. In this paper, the new method for adaptively detecting event based on Allen temporal algebra and external information support is presented. The temporal information is captured by presenting events as the temporal sequences using a lexicon of non-ambiguous temporal patterns. These sequences are then exploited to mine undiscovered sequences with external text information supports by using class associate rules mining technique. By modeling each pattern with linguistic part and perceptual part those work independently and connect together via transformer, it is easy to deploy this method to any new domain (e.g baseball, basketball, tennis, etc.) with a few changes in perceptual part and transformer. Thus the proposed method not only can work well in unwell structured environments but also can be able to adapt itself to new domains without the need (or with a few modification) for external re-programming, re-configuring and re-adjusting. Experimental results that are carried on more than 30 hours of soccer video corpus captured at different broadcasters and conditions as well as compared with well-known related methods, demonstrated the efficiency, effectiveness, and robustness of the proposed method in both offline and online processes.