Visual Sensor Placement and Orientation Optimization for Surveillance Systems

Zhang, Guanglei; Dong, Bin; Zheng, Jichuan

doi:10.1109/bwcca.2015.19

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…There are different approaches regarding placement optimization works [22], [23], [24], [25]. The approach in [2] proposed a definition of camera quality of view and a coverage term to rate camera configurations in 2D.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections

Malhotra¹,

Singh²,

Dadlani³

et al. 2022

Preprint

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This paper proposes a method to compute camera 6 DoF poses to achieve a user defined coverage. The camera placement problem is modeled as a combinatorial optimization where given the maximum number of cameras, a camera set is selected from a larger pool of possible camera poses. We propose to minimize the squared error between the desired and the achieved coverage, and formulate the non-linear cost function as a mixed integer linear programming problem. A camera lens model is utilized to project the camera's view on a 3D voxel map to compute a coverage score which makes the optimization problem in real environments tractable. Experimental results in two real retail store environments demonstrate the better performance of the proposed formulation in terms of coverage and overlap for triangulation compared to existing methods.

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Section: Related Workmentioning

confidence: 99%

Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections

Malhotra¹,

Singh²,

Dadlani³

et al. 2022

Preprint

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“…As the Internet of Things (IoT) continues to expand and sensor networks become more prevalent in various applications, the task of optimizing sensor utilization within specific environments with regard to certain key performance indicators is becoming increasingly important. One of the most important performance metrics is network coverage [1]. The overall coverage performance of the sensor network depends on the placement of each sensor.…”

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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“…Ali et al [18] propose a camera placement method to ensure extensive coverage while maintaining high-quality images and minimizing overlap between cameras. Zhang et al [19] introduce a novel field-of-view model resembling the human eye. They solve the optimization problem of visual sensor placement using a nonlinear programming algorithm.…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement Optimization of Visual Sensor Networks for Target Tracking Based on Multi-Objective Constraints

Zhou,

Deng,

Zhao

et al. 2024

Applied Sciences

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With the advancement of sensor technology, distributed processing technology, and wireless communication, Visual Sensor Networks (VSNs) are widely used. However, VSNs also have flaws such as poor data synchronization, limited node resources, and complicated node management. Thus, this paper proposes a sensor placement optimization method to save network resources and facilitate management. First, some necessary models are established, including the sensor model, the space model, the coverage model, and the reconstruction error model, and a dimensionality reduction search method is proposed. Next, following the creation of a multi-objective optimization function to balance reconstruction error and coverage, a clever optimization algorithm that combines the benefits of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is applied. Finally, comparison studies validate the methodology presented in this paper, and the combined algorithm can enhance optimization effect while relatively reducing running time. In addition, a sensor coverage method for large-range target space with obstacles is discussed.

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Visual Sensor Placement and Orientation Optimization for Surveillance Systems

Cited by 12 publications

References 11 publications

Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections

Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections

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

Sensor Placement Optimization of Visual Sensor Networks for Target Tracking Based on Multi-Objective Constraints

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