基于感知概念和有限状态机的体育视频语义内容分析模型

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

视频内容自动分析领域中,关键的挑战在于如何识别重要对象和如何建模对象之间的时空关系.本文基于感知概念(Perception Concepts,简称PCs)和有限状态机(Finite State Machines,简称FSMs)提出一种语义内容分析模型自动描述和探测体育视频中有意义的语义内容.根据体育视频中可识别的特征元素,定义PCs来表示视频中重要的语义模式;设计PC-FSM模型来描述PCs间的时空关系;采用一个图匹配方法自动探测视频中的高层语义.本文提出的方法使用户能够根据其自身的兴趣和知识设计体育视频的查询描述,并将语义内容探测问题转换为图匹配问题.实验结果验证了本文提出的方法的有效性.

In automatic video content analysis domain, the key challenges are how to recogmze important objects anti now to model the spatiotemporal relationships between them. This paper 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 high- lights and transfer the detection problem into a graph matching problem. Experimental results are used to illustrate the potential of this approach.