A Sparse Representation-Based Deployment Method for Optimizing the Observation Quality of Camera Networks

Sensors

Chang Wang¹  Fei Qi¹  Guangming Shi² 

[1] School of Electronic Engineering, Xidian University, Xi’an, Shaanxi, 710071, China;

关键词: camera networks;    deployment;    nodes layout and assignment;    sparse representation;    anisotropic sensing model;    observation quality;   

DOI  :  10.3390/s130911453

来源: mdpi

【 摘 要 】

Deployment is a critical issue affecting the quality of service of camera networks. The deployment aims at adopting the least number of cameras to cover the whole scene, which may have obstacles to occlude the line of sight, with expected observation quality. This is generally formulated as a non-convex optimization problem, which is hard to solve in polynomial time. In this paper, we propose an efficient convex solution for deployment optimizing the observation quality based on a novel anisotropic sensing model of cameras, which provides a reliable measurement of the observation quality. The deployment is formulated as the selection of a subset of nodes from a redundant initial deployment with numerous cameras, which is an ℓ₀ minimization problem. Then, we relax this non-convex optimization to a convex ℓ₁ minimization employing the sparse representation. Therefore, the high quality deployment is efficiently obtained via convex optimization. Simulation results confirm the effectiveness of the proposed camera deployment algorithms.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.