Optimal Camera Placement for Motion Capture Systems

Rahimian, Pooya; Kearney, Joseph K.

doi:10.1109/tvcg.2016.2637334

Cited by 53 publications

(32 citation statements)

References 23 publications

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“…Building on previous research, camera placement optimization techniques were further leveraged for a series of domain-specific applications. For example, the camera placement problem was reformulated to optimize the performance of visual analytics (e.g., reconstruction and motion captures) (Olague and Mohr 2002;Rahimian and Kearney 2017) and automated surveillance systems in urban areas (Bodor et al 2007;Szaloki et al 2013). In the construction domain, as the first step to addressing the AGP for optimal camera placement, the camera's coverage was computationally modeled to understand and evaluate the visibility of installed cameras.…”

Section: Art Gallery Problems For Camera Placement and Current Practimentioning

confidence: 99%

Systematic Camera Placement Framework for Operation-Level Visual Monitoring on Construction Jobsites

Kim

Ham

Chung³

et al. 2019

J. Constr. Eng. Manage.

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This paper proposes a camera placement framework that incorporates characteristics of complex construction jobsites. The proposed framework consists of three main processes: (1) identification of visual monitoring determinants, influencing factors, and camera placement conditions; (2) problem definition and mathematical modeling; and (3) hybrid simulation-optimization of camera placement on construction jobsites. To evaluate the performance of the proposed framework, a case study for an actual construction jobsite was performed. The results of the case study show the potential of the proposed framework to find the optimal number, types, locations, and orientations of cameras for jobsite visual monitoring. This research makes three main contributions: (1) identification of critical elements to be considered when installing fixed cameras on complex construction jobsites; (2) development of a systematic framework for camera placement on jobsites; and (3) visualization and quantification of multiple camera network designs and their performance (i.e., total costs and visible coverages) before installing cameras at actual jobsites.

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Section: Art Gallery Problems For Camera Placement and Current Practimentioning

confidence: 99%

Systematic Camera Placement Framework for Operation-Level Visual Monitoring on Construction Jobsites

Kim

Ham

Chung³

et al. 2019

J. Constr. Eng. Manage.

View full text Add to dashboard Cite

show abstract

“…After initialization, the subsequent heating and cooling of metal is imitated in the code by "raising temperature"-decreasing the viability of solutions-followed by "cooling down"-increasing the viability of solutions. Morsly et al [35] and Rahimian and Kearney [36] note SA as applied to the SCP and choosing viewpoints. The created set is then added to or removed from to achieve the desired coverage.…”

Section: Simulated Annealingmentioning

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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“…The sUAV SfM used in Italy builds off previous work by Palmer et al [28]. Various computational algorithms optimize what the sUAV needs to "see" as well as the best route to do so [23,[29][30][31][32][33]. The path planning algorithm, follows a weighted Traveling Salesman Problem (TSP).…”

Section: Optimized View and Path Planning Algorithmsmentioning

confidence: 99%

Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto

et al. 2019

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A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.5 caused significant damage to many small towns in the area. After each earthquake in 2016 (24 August and 26 October), automated small unmanned aerial vehicles (sUAV) acquired valuable imagery data for post-hazard reconnaissance in the mountain village of Pescara del Tronto, and were applied to 3D reconstruction using Structure-from-Motion (SfM). In July 2018, the site was again monitored to obtain additional imagery data capturing changes since the last visit following the 30 October 2016 Earthquake. A genetic-based mission-planning algorithm that delivers optimal viewpoints and path planning was field tested and reduced the required photos for 3D reconstruction by 9.1%. The optimized 3D model provides a better understanding of the current conditions of the village, when compared to the nadir models, by containing fewer holes on angled surfaces, including an additional 17% surface area, and with a comparable ground-sampling distance (GSD) of ≈2.4 cm/px (≈1.5 cm/px when adjusted for camera pixel density). The resulting three time-lapse models provide valuable metrics for ground motion, progression of damage, resilience of the village, and the recovery progress over a span of two years.

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Optimal Camera Placement for Motion Capture Systems

Cited by 53 publications

References 23 publications

Systematic Camera Placement Framework for Operation-Level Visual Monitoring on Construction Jobsites

Systematic Camera Placement Framework for Operation-Level Visual Monitoring on Construction Jobsites

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto

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