Camera Placement in Smart Cities for Maximizing Weighted Coverage With Budget Limit

Jun, Sungbum; Chang, Tai‐Woo; Jeong, Hanil; Lee, Seokcheon

doi:10.1109/jsen.2017.2723481

Cited by 22 publications

(13 citation statements)

References 25 publications

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“…Physical verification of simulation results is an essential step for continued application of research results and occurs periodically in the literature for photogrammetry and SfM [45,[53][54][55][56][57][58]. The algorithm set is tested at the Rock Canyon collection dike in Provo, UT, USA.…”

Section: Physical Test Flightsmentioning

confidence: 99%

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

et al. 2020

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Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms α (iteration number) and β (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR.

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Section: Physical Test Flightsmentioning

confidence: 99%

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

et al. 2020

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“…Greedy algorithm based on an Image-based model [3] Simulated annealing (SA)-based area coverage enhancing algorithm [12] Max. Weighted Coverage Binary integer programming (BIP) [13] Genetic algorithm, Octree-based search, and Random search [14] Collaboration-based local search algorithm (COLSA) [15] Max. Weighted Coverage, Min.…”

Section: Dmentioning

confidence: 99%

“…Number of Camera Binary quadratic programming (BQP) [16] Min. Cost Particle swarm optimization (PSO) [17] In terms of objective functions, the maximization of coverage has been widely studied and many solution approaches have been suggested [2][3][4][7][8][9][11][12][13][14][15][16]. Furthermore, multi-objective problems, including maximizing coverage and visibility while minimizing the total cost, have been studied [10,11].…”

Section: Dmentioning

confidence: 99%

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Placing Visual Sensors Using Heuristic Algorithms for Bridge Surveillance

2018

Self Cite

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This study addresses the camera placement problem for bridge surveillance and proposes solutions that minimize the cost while satisfying the minimum coverage level. We discuss the field of view of cameras in the three-dimensional space. We also consider occlusions, the characteristics of surveillance targets, and different pan-tilt-zoom cameras in the visibility test. To solve the camera placement problem while minimizing the total cost, we propose a genetic algorithm (GA) and a uniqueness score with a local search algorithm (ULA). Problem sets for a large-scale dimension scenario are generated based on the data of actual bridges in the Republic of Korea. For three simulation sets and a case study of Samoonjin Bridge, the proposed ULA yields better results than GA.

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“…Determining the optimal sensor configuration for multisensor networks is only a partially resolved problem [4]. One of the major challenges in sensor planning is accounting for dynamic occlusions due to a changing environment [5], [6]. In human-robot environments, an occlusion occurs when a sensor (such as a ceiling mounted camera) loses sight of the target workspace due to obstruction by objects, human beings or the robot itself [7].…”

Section: Introductionmentioning

confidence: 99%

Robust Optimal Sensor Planning for Occlusion Handling in Dynamic Robotic Environments

Mohan

Jager

2019

IEEE Sensors J.

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DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Camera Placement in Smart Cities for Maximizing Weighted Coverage With Budget Limit

Cited by 22 publications

References 25 publications

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

Placing Visual Sensors Using Heuristic Algorithms for Bridge Surveillance

Robust Optimal Sensor Planning for Occlusion Handling in Dynamic Robotic Environments

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