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2007
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A Semantic Content Analysis Model for Sports Video Based on Perception Concepts and Finite State Machines

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A Semantic Content Analysis Model for Sports Video Based on Perception Concepts and Finite State Machines
In automatic video content analysis domain, the key challenges are how to recognize important objects and how to model the spatiotemporal relationships between them. In this paper we propose a semantic content analysis model based on Perception Concepts (PCs) and Finite State Machines (FSMs) to automatically describe and detect significant semantic content within sports video. PCs are defined to represent important semantic patterns for sports videos based on identifiable feature elements. PC-FSM models are designed to describe spatiotemporal relationships between PCs. And graph matching method is used to detect high-level semantic automatically. A particular strength of this approach is that users are able to design their own highlights and transfer the detection problem into a graph matching problem. Experimental results are used to illustrate the potential ofthis approach.
Liang Bai, Songyang Lao, Gareth J. F. Jones, Alan
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICMCS
Authors Liang Bai, Songyang Lao, Gareth J. F. Jones, Alan F. Smeaton
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