VOE: A new sparsity-based camera network placement framework

被引:0
|
作者
Fu, Yi-Ge [1 ]
Zhou, Jie [1 ,2 ,3 ]
机构
[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
[2] Tsinghua Natl Lab Informat Sci & Technol, Beijing, Peoples R China
[3] State Key Lab Intelligent Technol & Syst, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Camera network placement; Cascading filter model; Sparsity; Stepwise framework; COVERAGE; SURVEILLANCE;
D O I
10.1016/j.neucom.2016.02.065
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 an l(1)-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. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:184 / 194
页数:11
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