VOE: A new sparsity-based camera network placement framework

VOE: A new sparsity-based camera network placement framework
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VOE:一种新的基于稀疏性的摄像机网络放置框架

DOI:
10.1016/j.neucom.2016.02.065
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发表时间:
2016-07
期刊:
影响因子:
6
通讯作者:
Zhou Jie
Zhou Jie
中科院分区:
计算机科学2区
文献类型:
--
作者:
Fu Yi-Ge;Zhou Jie

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在本文中,我们提出了一种基于逐步稀疏性的摄像机网络布局框架。与之前大多数针对特定任务开发的方法不同,我们的方法是通用的,可以很好地概括不同的应用场景。我们的方法有三个步骤:可见性分析、优化和评估(VOE),这三个步骤是顺序和迭代的。首先,我们使用级联可见性过滤器模型构建可见性矩阵,其中每列描述监视区域的外观表示。然后,我们将摄像机网络布局问题表述为一个稀疏表示问题,并使用1- 1优化算法得到可行解。我们的框架足够通用,适用于实际应用中的各种目标。实验结果表明了该框架的有效性和高效性。
In this paper, we propose a stepwise sparsity-based framework for camera network placement. Unlike most previous methods which are developed for specific tasks, our approach is universal and can generalize well for different application scenarios. There are three steps in our approach: visibility analysis, optimization and evaluation (VOE), which are employed sequentially and iteratively. First, we use a cascaded visibility filter model to construct a visibility matrix, where each column describes the appearance representation of the surveillance area. Then, we formulate camera network layout as a sparse representation problem, and employ anl1-optimization algorithm to obtain a feasible solution. Our framework is general enough and applicable to various objectives in practical applications. Experiment results are presented to show the effectiveness and efficiency of the proposed framework.
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