Optimal camera placement for total coverage

González-Barbosa, José-Joel; Garcia-Ramírez, Teresa; Salas, Joaquín; Hurtado-Ramos, Juan B.; Rico-Jimenez, Jose J.

doi:10.1109/robot.2009.5152761

Cited by 59 publications

(56 citation statements)

References 13 publications

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“…The computational cost of solving this LBP is reduced by a divideand-conquer strategy using the Parisian evolutionary computation approach of Dunn et al [DOL06]. This LBP can be extended to allow for different types of cameras (directional and omnidirectional) with different imaging models [GB09]. Limited Budget Placement: Another placement objective that is optimized is the coverage/visibility of an ROI under a strict limitation on the number of cameras (or equivalently a limited budget).…”

Section: Related Workmentioning

confidence: 99%

Designing Camera Networks by Convex Quadratic Programming

Ghanem

Cao

Wonka

2015

Computer Graphics Forum

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Figure 1: Example result of our proposed optimal camera placement framework. In a particular scenario, the user inputs a 3D floorplan that can be generated by processing an overhead 2D floorplan using the user-friendly GUI we developed. After setting certain camera parameters (e.g. field-of-view and depth-of-field), our approach computes a placement solution that can either maximize 3D floorplan coverage with a limited number of cameras or minimize the number of cameras needed to cover the entire floorplan. Unlike other placement methods, our approach is computationally efficient because it solves a constrained convex quadratic program. It also allows pairwise camera interactions to be directly encoded, which is quite useful for multiview applications, such as 3D reconstruction and surveillance. AbstractIn this paper, we study the problem of automatic camera placement for computer graphics and computer vision applications. We extend the problem formulations of previous work by proposing a novel way to incorporate visibility constraints and camera-to-camera relationships. For example, the placement solution can be encouraged to have cameras that image the same important locations from different viewing directions, which can enable reconstruction and surveillance tasks to perform better. We show that the general camera placement problem can be formulated mathematically as a convex binary quadratic program (BQP) under linear constraints. Moreover, we propose an optimization strategy with a favorable trade-off between speed and solution quality. Our solution is almost as fast as a greedy treatment of the problem, but the quality is significantly higher, so much so that it is comparable to exact solutions that take orders of magnitude more computation time. Because it is computationally attractive, our method also allows users to explore the space of solutions for variations in input parameters. To evaluate its effectiveness, we show a range of 3D results on real-world floorplans (garage, hotel, mall, and airport).

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Section: Related Workmentioning

confidence: 99%

Designing Camera Networks by Convex Quadratic Programming

Ghanem

Cao

Wonka

2015

Computer Graphics Forum

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“…FoV overlapping indicates that two or more visual sensors are related and such relation can be exploited in different ways, as in node localization [15] and when composing communication topologies [16]. In some cases, however, FoV overlapping may be viewed as a suboptimal configuration that should be avoided [17] [18], especially in controlled environments where unconstrained nodes are deployed.…”

Section: Related Workmentioning

confidence: 99%

Selecting redundant nodes when addressing availability in wireless visual sensor networks

Costa

Silva

Guedes

et al. 2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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As Wireless Sensor Networks have been employed to support critical monitoring applications, network availability has become a major design concern. In these networks, redundancy can be exploited to enhance the attainable availability level, where redundant sensors can replace faulty nodes. When cameraenabled sensors are deployed to retrieve visual information, the perception of redundancy changes considerably, since the redundancy of visual sensors depends on the monitoring requirements of the applications. In such context, characteristics as deployment density, viewing angle and sensing range are relevant when planning wireless sensor network applications, directly impacting in the number of redundant nodes. We propose an algorithm to select redundant nodes in Wireless Visual Sensor Networks, according to the application requirements. Moreover, we discuss how parameters of the deployed network can influence on the number of redundant nodes.

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“…Table 1 provides the main categories of approximation methods tested in this paper and their usage in existing works. BIP/LP Greedy Heuristic MC SDP [15] X [3] X [8] X X [14] X X [28] X X [13] X [27] X X [7] X X X Table 1. Various approaches for camera placement…”

Section: Contributionsmentioning

confidence: 99%

“…This strategy naturally leads to combinatorial problems with the camera, environment, and traffic models encoded in different integral constraints and objective functions. Efforts have been made to formulate the discrete camera placement problems using standard binary linear programming [15,28,13] and quadratic programming [7]. While both formulations result in NP-hard problems, a myriad of practical solutions including Binary Integer Programming solvers (BIP), greedy approach, greedy heuristics, Monte Carlo (MC) simulations and Semi-Definite Programming relaxations (SDP) have been proposed [15,3,8,14,28,13,27,7].…”

Section: Introductionmentioning

confidence: 99%

Approximate techniques in solving optimal camera placement problems

Zhao

Haws

Yoshida

et al. 2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

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While the theoretical foundation of optimal camera placement has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor network. The discrete camera placement problem is NP-hard and many approximate solutions have been independently studied. The goal of this paper is to provide a comprehensive framework in comparing the merits of these techniques. We consider two general classes of camera placement problems and adapt some of the most commonly used approximation techniques in solving them. The accuracy, efficiency and scalability of each technique are analyzed and compared in depth. Extensive experimental results are provided to illustrate the strength and weakness of each method.

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Optimal camera placement for total coverage

Cited by 59 publications

References 13 publications

Designing Camera Networks by Convex Quadratic Programming

Designing Camera Networks by Convex Quadratic Programming

Selecting redundant nodes when addressing availability in wireless visual sensor networks

Approximate techniques in solving optimal camera placement problems

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