A Generic and Scalable Approach to Maximize Coverage in Diverse Indoor and Outdoor Multicamera Surveillance Scenarios

Suresh, M. S. Sumi; Menon, Vivek

doi:10.1109/tsmc.2022.3194209

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Three-dimensional environment models provide a more accurate representation, but are associated with higher computational costs [17]. 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].…”

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%

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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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