Maximizing Camera Coverage in Multicamera Surveillance Networks

Suresh, M. S. Sumi; Narayanan, Athi; Menon, Vivek

doi:10.1109/jsen.2020.2992076

Cited by 37 publications

(9 citation statements)

References 16 publications

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“…Yang et al (2018) used 2D polygons to represent the layout of construction sites and optimized the placement of surveillance cameras toward better coverage of the construction areas. Similarly, Altahir et al (2018) and Suresh et al (2020) used 2D shapes to model buildings as obstacles in an urban context and optimize multicameras' placement to improve their coverage of public areas.…”

Section: Scenario Definition and Sensor Modelingmentioning

confidence: 99%

“…(2018) and Suresh et al. (2020) used 2D shapes to model buildings as obstacles in an urban context and optimize multicameras’ placement to improve their coverage of public areas.…”

Section: Introductionmentioning

confidence: 99%

“…As such, the detection accuracy can be quantified by estimating to what extent target areas overlap with the sensors’ field of view (FOV). Two‐dimensional approaches play an essential role during the early stages when computational power was limited and are still active in solving placement optimization of multiple camera system (Suresh et al., 2020). However, the simplified representation of scenarios and models may compromise the accuracy of optimization results.…”

Section: Introductionmentioning

confidence: 99%

“…TA B L E 1Static Hiromori et al, 2013;Altahir et al, 2018;Yang et al, 2018;Geissler & Gräfe 2019;Suresh et al, 2020 …”

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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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Section: Scenario Definition and Sensor Modelingmentioning

confidence: 99%

“…(2018) and Suresh et al. (2020) used 2D shapes to model buildings as obstacles in an urban context and optimize multicameras’ placement to improve their coverage of public areas.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…TA B L E 1Static Hiromori et al, 2013;Altahir et al, 2018;Yang et al, 2018;Geissler & Gräfe 2019;Suresh et al, 2020 …”

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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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“…However, there are very limited studies considering camera FoV in a production environment. Currently studies [21]- [23] mainly focuses on calibration when facing limited object visibility with fixed camera FoV under challenging circumstances, such as monitoring and surveillance. These applications do not require relocation or rapid decision about camera positioning.…”

Section: Introductionmentioning

confidence: 99%

Deep Dynamic Layout Optimization of Photogrammetry Camera Position Based on Digital Twin

Wang,

Kendall

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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The photogrammetry system has been widely used in industrial manufacturing applications, such as high-precision assembly, reverse engineering and additive manufacturing. In order to meet the demand of the product variety and short product lifecycle, the factory facilities including photogrammetry devices, should be relocated in response to rapid change in mechanical structure and hardware integration. Nevertheless, the camera position of the photogrammetry system is difficult to select to guarantee an optimal field of view (FoV) coverage of retro-reflective targets during the whole production horizon. Especially in a reconfigurable manufacturing work cell, scaling and calibration of a photogrammetry system requires professional skills and these would cost tremendous labour for rapid configuration each time. In this paper, we propose a novel deep optimisation framework for the photogrammetry camera position for the dynamic layout design based on digital twin. The optimisation framework follows an effective coarse-to-fine procedure to evaluate the FoV visibility over the target frame. In addition, the deep Q-learning algorithm is utilised to find the maximum FoV coverage and avoid collision. Three experiments are implemented to verify the application feasibility of the proposed deep camera position optimisation framework.Note to Practitioners-Large-volume in-process metrology is an essential element in flexible manufacturing systems. Quality of large-volume measurement relies heavily on target visibility within its field of view. In a compact industrial robotic cell, this is extremely challenging as the robot would take the primary position and causing view blockage throughout its operation. This makes the simultaneous monitoring of robot head and the work piece key feature extremely difficult. Manual trial-anderror positioning approach is lengthy and requires high level of expertise, due to both safety and spatial concerns. We approached this problem by simulating the camera's view in a digital twin environment and maximising the target visibility throughout the full operation cycle. The generic framework can provide guidance in metrology setup within automated manufacturing environment, accelerate the system commissioning time, remove dependency of skill level and expand the capability for flexible/reconfigurable manufacturing systems. Although V-STARS camera are used in this application, the framework can be applied for other types of vision systems that requires field of view.

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Exploring the Optimal Camera Placement Problem and Its Relationship with the Set Covering Problem

Almousa,

Ehrgott,

Kheiri

2024

Lecture Notes in Operations Research

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Maximizing Camera Coverage in Multicamera Surveillance Networks

Cited by 37 publications

References 16 publications

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

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

Deep Dynamic Layout Optimization of Photogrammetry Camera Position Based on Digital Twin

Exploring the Optimal Camera Placement Problem and Its Relationship with the Set Covering Problem

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