具有重叠视场的分布式智能摄像机网络的局部图像质量度量

E. Shen, R. Hornsey
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引用次数: 7

摘要

使用基于本地(摄像机)级别度量的简单规则,实现了一组摄像机选择模板,用于12个摄像机向内看的分布式智能摄像机网络。局部度量表示对感兴趣目标的给定相机节点的检测质量,并基于可测量的目标参数。为了了解摄像机选择的有效性,设计了一个由全局(系统)级别度量组成的分析框架。相机选择方法能够在使用可用相机总数的子集时保持理想的全局度量性能。即使系统受到单个摄像机或单个遮挡目标的干扰,这也是正确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local image quality metric for a distributed smart camera network with overlapping FOVs
A set of camera selection templates, using simple rules based on a local (camera) level metric, are implemented for a twelve camera inward-looking distributed smart camera network. The local metric represents the quality of detection for a given camera node of the target-of-interest and is based on a measurable target parameter. To understand the effectiveness of the camera selections, an analytical framework consisting of a global (system) level metric has been designed. The camera selection methods are able to maintain a desirable global metric performance while using a subset of the total cameras available. This is true even when the system undergoes perturbation by the loss of a single camera or by a single occluding target.
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