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A Sparse Representation-Based Deployment Method for Optimizing the Observation Quality of Camera Networks
School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, 710071, China
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Received: 3 July 2013; in revised form: 13 August 2013 / Accepted: 14 August 2013 / Published: 28 August 2013
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.
Keywords: camera networks; deployment; nodes layout and assignment; sparse representation; anisotropic sensing model; observation quality
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Cite This Article
MDPI and ACS Style
Wang, C.; Qi, F.; Shi, G.; Wang, X. A Sparse Representation-Based Deployment Method for Optimizing the Observation Quality of Camera Networks. Sensors 2013, 13, 11453-11475.
Wang C, Qi F, Shi G, Wang X. A Sparse Representation-Based Deployment Method for Optimizing the Observation Quality of Camera Networks. Sensors. 2013; 13(9):11453-11475.
Wang, Chang; Qi, Fei; Shi, Guangming; Wang, Xiaotian. 2013. "A Sparse Representation-Based Deployment Method for Optimizing the Observation Quality of Camera Networks." Sensors 13, no. 9: 11453-11475.