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Sports video analysis has received special attention from researchers due to its high popularity and general interest on semantic analysis. Hence, soccer videos represent an interesting field for research allowing many types of applications: indexing, summarization, players' behavior recognition and so forth. Many approaches have been applied for field extraction and recognition, arc and goalmouth...
Sub-pixel detection of target points is the performance bottleneck in camera calibration. Traditional algorithms are computational expensive or low precision when we do camera calibration in sport video analysis. In this paper, we propose a new algorithm to detect the grid-like target (i.e. tennis court in TV broadcasting). It has 3 parts: (1) color histogram based interested point classifier making...
Classifying video content into different semantic granularities is a possible way for flexible video indexing, browsing and retrieval. In this paper, a placed kick refinement algorithm is proposed after semantic based event detection or manually annotation. The placed kick event is further classified into following three types: free kick, corner kick and penalty according to the ball and field lines...
Objects like field lines and players are important for semantic analysis in soccer video. In this paper, we propose effective methods for field lines and players detection and recognition. Regions of field lines and players are first segmented from shot of wide angle view. Gray value top-hat transform is then performed on the segmented region to detect field lines. Mid-lines, end-lines and penalty-lines...
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