title: A sparse representation-based deployment method for optimizing the observation quality of camera networks.
abstract: 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.
authors.affiliation: School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China. cwang@mail.xidian.edu.cn
authors.forename: Chang
authors.initials: C
authors.lastname: Wang
authors.forename: Fei
authors.initials: F
authors.lastname: Qi
authors.forename: Guangming
authors.initials: G
authors.lastname: Shi
authors.forename: Xiaotian
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003195:Computer Communication Networks; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*