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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.


DOF for the camera.
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f5-sensors-14-01988: DOF for the camera.

Mentions: In this section, we determine the constraints on the camera's viewpoints when we request that all the points of the surveillance area must be sharp (in focus) in some surveillance camera's FOV. For any camera, there is only one plane on which the camera can precisely focus, and the point object in any other plane is imaged as a disk (known as the blur spot) rather than a point. When the diameter of blur spot is sufficiently small, the image disk is indistinguishable from a point. The diameter of the blur spot is known as the acceptable circle of confusion, or simply as the circle of confusion (CoC). We can easily induce that there is a region of acceptable sharpness between two planes on either side of the plane of focus which is illustrated in Figure 5. The region is known as depth of field (DOF).


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

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

DOF for the camera.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-14-01988: DOF for the camera.
Mentions: In this section, we determine the constraints on the camera's viewpoints when we request that all the points of the surveillance area must be sharp (in focus) in some surveillance camera's FOV. For any camera, there is only one plane on which the camera can precisely focus, and the point object in any other plane is imaged as a disk (known as the blur spot) rather than a point. When the diameter of blur spot is sufficiently small, the image disk is indistinguishable from a point. The diameter of the blur spot is known as the acceptable circle of confusion, or simply as the circle of confusion (CoC). We can easily induce that there is a region of acceptable sharpness between two planes on either side of the plane of focus which is illustrated in Figure 5. The region is known as depth of field (DOF).

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.