Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/42170
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dc.titleA mid-level representation framework for semantic sports video analysis
dc.contributor.authorDuan, L.-Y.
dc.contributor.authorXu, M.
dc.contributor.authorChua, T.-S.
dc.contributor.authorTian, Q.
dc.contributor.authorXu, C.-S.
dc.date.accessioned2013-07-04T08:45:07Z
dc.date.available2013-07-04T08:45:07Z
dc.date.issued2003
dc.identifier.citationDuan, 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.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42170
dc.description.abstractSports 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.
dc.sourceScopus
dc.subjectEvents
dc.subjectMid-level representation
dc.subjectSemantics
dc.subjectSports video
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings of the ACM International Multimedia Conference and Exhibition
dc.description.page33-44
dc.identifier.isiutNOT_IN_WOS
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