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This paper presents a system for automatically detecting and analyzing complex player actions in moving background sports video sequences, aiming at action-based sports videos indexing and providing kinematic measurements for coach assistance and performance improvement. The system works in a coarse-to-fine fashion. For an input video, in the coarse granularity level, we automatically segment the highlights, that is, the video clips containing the desired action as summaries for general user viewing purposes; in the middle granularity level, we recognize the action types to support action-based video indexing and retrieval; and finally in the fine granularity level, the critical kinematic parameters of player action are obtained for sports professionals' training purposes. However, the complex and dynamic background of sports videos and the complexity of player actions bring considerable difficulty to the automatic analysis. To fulfill such a challenging task, robust algorithms including global motion estimation with adaptive outliers filtering, object segmentation based on adaptive background construction, and automatic human body tracking are proposed in this paper. Two visual analyzing tools: motion panorama and overlay composition, are also introduced. Real diving and jump game videos are used to test the proposed system and algorithms, and the extensive and encouraging experimental results show their effectiveness.