Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections

Malhotra, Akshay; Singh, Dhananjay; Dadlani, Tushar; Morales, Luis Yoichi

doi:10.1109/icra46639.2022.9812042

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…These models consider how the height of obstacles impacts the field of visibility from specific points, albeit incurring additional computational costs. Past research has approximated three-dimensional problems using fixed sensor height and limiting the search space to one plane [40], [46], [49]. It is important to consider the topography of the environment and the obstacles that occlude the sensory area [50].…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Advancing Indoor Environment Modeling: A Comparative Study of Evolutionary Algorithms for Optimal Sensor Placement

Banov,

Pinčić,

Lenac

et al. 2024

Preprint

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This study advances indoor environment modeling by focusing on the optimal placement of sensors. Our approach involves creating a detailed environment model from a 3D point cloud by identifying spatial boundaries and furniture in indoor spaces, which are then represented as a series of polygons. To validate our method, we compare its performance against ground truth data, demonstrating high accuracy in both simple and complex environments. The core of our study is a comprehensive experiment that evaluates the effectiveness of three evolutionary nature-inspired genetic and three metaheuristic iterative optimization algorithms in solving the sensor placement problem in a complex environment scenario. We perform a statistical analysis to understand the impact of the choice of optimization algorithm and the number of sensors on the achieved spatial coverage. This analysis provides insights into the comparative effectiveness of various evolutionary algorithms in enhancing sensor network design within intricate indoor spaces. In particular, the Artificial Bee Colony algorithm consistently delivered superior results.

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Section: A Related Workmentioning

confidence: 99%

“…As for the algorithm parameters, the swarm size was determined empirically by testing different swarm sizes (10,20,30,40,50) in problems with two and nine independent variables. No statistically significant differences were found in the results of comparing different swarm sizes, so the swarm size of 10 was used for all algorithms.…”

Section: Sensor Placementmentioning

confidence: 99%

Advancing Indoor Environment Modeling: A Comparative Study of Evolutionary Algorithms for Optimal Sensor Placement

Banov,

Pinčić,

Lenac

et al. 2024

Preprint

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“…To optimize the configuration of the multicamera surveillance system, 3D point clouds were used to depict the indoor environment in Malhotra et al. (2022). They concluded that 3D point cloud data acting as optimization background can better address cameras’ placement in real environments.…”

Section: Introductionmentioning

confidence: 99%

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

Ma,

Zheng,

Wang

et al. 2024

Computer aided Civil Eng

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Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real‐life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes a virtual‐real‐fusion simulation (VRFS) framework that combines traffic simulation and point clouds of real‐world road environment to optimize RSUs’ deployment. Point clouds and triangular meshes are used to model static and dynamic obstacles, respectively. A structure‐retained spherical projection method is developed to efficiently emulate RSUs’ data collection. Based on the developed VRFS, the probabilistic occupancy maps (POM) are created to represent traffic scenarios. The POM‐based cross entropy (CE) is proposed as the surrogate metric for evaluating the detection performance of cooperative RSUs. The Bayesian optimizer is applied to optimize the RSUs’ placement parameters (decision variables) by minimizing CE. Test results show that it is viable to use the POM‐based CE as a proxy for evaluating cooperative RSUs’ sensing performance. Considering the occlusion effect adds to the efficacy of POM‐based CE as a surrogate metric. Compared with traffic volume, the adverse effect of the proportion of large vehicles on RSUs’ detection performance is more significant. There are no significant patterns regarding how the optimized RSU positions vary with traffic parameters. The comparisons with existing methods further verify the importance of considering both static and dynamic occlusions in optimizing RSUs’ placement. Besides, the proposed method can yield better optimization results more efficiently than existing approaches.

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“…In previous research, spaces are often simplified to the level of a floor plan or two-dimensional boundaries [18][19][20][21][22][23][24][25][26]. As an improvement of the two-dimensional environment models, three-dimensional models were mostly described with elevation maps [27][28][29]. In our previous research, we proposed three-dimensional environment models and a framework for optimizing sensor placement [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Sensors can have binary or probabilistic sensing ability. The sensing ability of a sensor refers to whether it can perceive an object in the case of binary coverage [19][20][21][22][23][24][25][26][29][30][31], or how effectively it can perceive it in the case of probabilistic coverage, which provides a more refined model [15,16,28,32,33]. In addition to detection capability, the shape of the area covered by the sensor is also important.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Sensor Placement: Recursive Exhaustive Search and Stochastic Optimization Parameter Impact Analysis

Banov,

Pinčić,

Lenac

et al. 2024

Applied Sciences

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This study presents a comprehensive approach for single sensor placement optimization in two-dimensional and three-dimensional spaces. A traditional exhaustive search technique and a novel method called recursive exhaustive search are used to place a sensor in a way that maximizes the area coverage metric. Exhaustive search provides a baseline by methodically evaluating all potential placements, while recursive exhaustive search innovates by segmenting the search process into more manageable, recursive steps. Our findings highlight the significant impact of two key parameters, the number of evaluations and the rasterization value, on the achieved coverage and computation time. The results show that the right choice of parameters can significantly reduce the computational effort without compromising the quality of the solution. This underlines the critical need for a balanced approach that considers both computational complexity and placement efficacy. We show that exhaustive search is not feasible for three-dimensional environment models and propose to establish a modified exhaustive search method as a ground truth for the single sensor placement problem. We then explore nature-inspired genetic algorithms and the impact of the number of evaluations of the optimization function for these algorithms on both accuracy and computational cost.

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Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections

Cited by 8 publications

References 28 publications

Advancing Indoor Environment Modeling: A Comparative Study of Evolutionary Algorithms for Optimal Sensor Placement

Advancing Indoor Environment Modeling: A Comparative Study of Evolutionary Algorithms for Optimal Sensor Placement

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

A Novel Approach to Sensor Placement: Recursive Exhaustive Search and Stochastic Optimization Parameter Impact Analysis

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