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Surveillance of a 2D plane area with 3D deployed cameras.

Fu YG, Zhou J, Deng L - Sensors (Basel) (2014)

Bottom Line: As the use of camera networks has expanded, camera placement to satisfy some quality assurance parameters (such as a good coverage ratio, an acceptable resolution constraints, an acceptable cost as low as possible, etc.) has become an important problem.We model the problem under some more realistic assumptions: (1) deploy the cameras in the 3D space while the surveillance area is restricted to a 2D ground plane; (2) deploy the minimal number of cameras to get a maximum visual coverage under more constraints, such as field of view (FOV) of the cameras and the minimum resolution constraints.We can simultaneously optimize the number and the configuration of the cameras through the introduction of a regulation item in the cost function.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation, Tsinghua University, Beijing 100084, China. iamdafu@gmail.com.

ABSTRACT
As the use of camera networks has expanded, camera placement to satisfy some quality assurance parameters (such as a good coverage ratio, an acceptable resolution constraints, an acceptable cost as low as possible, etc.) has become an important problem. The discrete camera deployment problem is NP-hard and many heuristic methods have been proposed to solve it, most of which make very simple assumptions. In this paper, we propose a probability inspired binary Particle Swarm Optimization (PI-BPSO) algorithm to solve a homogeneous camera network placement problem. We model the problem under some more realistic assumptions: (1) deploy the cameras in the 3D space while the surveillance area is restricted to a 2D ground plane; (2) deploy the minimal number of cameras to get a maximum visual coverage under more constraints, such as field of view (FOV) of the cameras and the minimum resolution constraints. We can simultaneously optimize the number and the configuration of the cameras through the introduction of a regulation item in the cost function. The simulation results showed the effectiveness of the proposed PI-BPSO algorithm.

No MeSH data available.


A camera network placement strategy for a particle state which is a inverse process of the particle representation.
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f8-sensors-14-01988: A camera network placement strategy for a particle state which is a inverse process of the particle representation.

Mentions: The fitness of each particle is calculated according to the cost function G(C) of the camera network placement problem. At first we should transfer the particle's state to a camera network placement solution which is a inverse problem of the particle representation which is shown in Figure 8.


Surveillance of a 2D plane area with 3D deployed cameras.

Fu YG, Zhou J, Deng L - Sensors (Basel) (2014)

A camera network placement strategy for a particle state which is a inverse process of the particle representation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958228&req=5

f8-sensors-14-01988: A camera network placement strategy for a particle state which is a inverse process of the particle representation.
Mentions: The fitness of each particle is calculated according to the cost function G(C) of the camera network placement problem. At first we should transfer the particle's state to a camera network placement solution which is a inverse problem of the particle representation which is shown in Figure 8.

Bottom Line: As the use of camera networks has expanded, camera placement to satisfy some quality assurance parameters (such as a good coverage ratio, an acceptable resolution constraints, an acceptable cost as low as possible, etc.) has become an important problem.We model the problem under some more realistic assumptions: (1) deploy the cameras in the 3D space while the surveillance area is restricted to a 2D ground plane; (2) deploy the minimal number of cameras to get a maximum visual coverage under more constraints, such as field of view (FOV) of the cameras and the minimum resolution constraints.We can simultaneously optimize the number and the configuration of the cameras through the introduction of a regulation item in the cost function.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation, Tsinghua University, Beijing 100084, China. iamdafu@gmail.com.

ABSTRACT
As the use of camera networks has expanded, camera placement to satisfy some quality assurance parameters (such as a good coverage ratio, an acceptable resolution constraints, an acceptable cost as low as possible, etc.) has become an important problem. The discrete camera deployment problem is NP-hard and many heuristic methods have been proposed to solve it, most of which make very simple assumptions. In this paper, we propose a probability inspired binary Particle Swarm Optimization (PI-BPSO) algorithm to solve a homogeneous camera network placement problem. We model the problem under some more realistic assumptions: (1) deploy the cameras in the 3D space while the surveillance area is restricted to a 2D ground plane; (2) deploy the minimal number of cameras to get a maximum visual coverage under more constraints, such as field of view (FOV) of the cameras and the minimum resolution constraints. We can simultaneously optimize the number and the configuration of the cameras through the introduction of a regulation item in the cost function. The simulation results showed the effectiveness of the proposed PI-BPSO algorithm.

No MeSH data available.