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|Title:||A mid-level representation framework for semantic sports video analysis||Authors:||Duan, L.-Y.
|Issue Date:||2003||Citation:||Duan, L.-Y.,Xu, M.,Chua, T.-S.,Tian, Q.,Xu, C.-S. (2003). A mid-level representation framework for semantic sports video analysis. Proceedings of the ACM International Multimedia Conference and Exhibition : 33-44. ScholarBank@NUS Repository.||Abstract:||Sports video has been widely studied due to its tremendous commercial potentials. Despite encouraging results from various specific sports games, it is almost impossible to extend a system for a new sports game because they usually employ different sets of low-level features appropriate for the specific games and closely coupled with the use of game specific rules to detect events or highlights. There is a lack of internal representation and structure to be generic and applicable for many different sports. In this paper, we present a generic mid-level representation framework for semantic sports video analysis. The mid-level representation layer is introduced between the low-level audio-visual processing and high-level semantic analysis. It allows us to separate sports specific knowledge and rules from the low-level and mid-level feature extraction. This makes sports video analysis more efficient, effective, and less ad-hoc for various types of sports. To achieve robustness of the low-level feature analysis, a non-parametric clustering, mean shift procedure, has been successfully applied to both color and motion analysis. The proposed framework has been tested for five field-ball type sports covering duration of about 8 hours. Experiments have shown its robust performance in semantic analysis and event detection. We believe that the proposed mid-level representation framework can be used for event detection, highlight extraction, summarization and personalization of many types of sports video.||Source Title:||Proceedings of the ACM International Multimedia Conference and Exhibition||URI:||http://scholarbank.nus.edu.sg/handle/10635/42170|
|Appears in Collections:||Staff Publications|
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