Self-Reconfigurable Smart Camera Networks

SanMiguel, Juan C.; Micheloni, Christian; Shoop, Karen; Foresti, Gian Luca; Cavallaro, Andrea

doi:10.1109/mc.2014.133

Cited by 53 publications

(3 citation statements)

References 15 publications

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“…The first problem is the coverage maximisation problem, addressed when deploying camera networks and deciding where to position and orient each camera to maximise the observed area. This problem, also known as the Art Gallery problem, has been researched quite intensively [47,68,79,83]. To cover an area with a defined number of cameras, Fusco and Gupta [38] utilise a simple greedy algorithm.…”

Section: Problems Related To Omokcmentioning

confidence: 99%

A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems

Pianini

Pettinari

Casadei

et al. 2022

ACM Trans. Auton. Adapt. Syst.

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We focus on the online multi-object k -coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way. There is active research on OMOkC, particularly in the design of decentralised algorithms for solving it. We propose a new take on the issue: rather than classically developing new algorithms; we apply a macro-level paradigm, called aggregate computing , specifically designed to directly program the global behaviour of a whole ensemble of devices at once. To understand the potential of the application of aggregate computing to OMOkC, we extend the Alchemist simulator (supporting aggregate computing natively) with a novel toolchain component supporting the simulation of mobile robots. This way, we build a software engineering toolchain comprising language and simulation tooling for addressing OMOkC. Finally, we exercise our approach and related toolchain by introducing new algorithms for OMOkC; we show that they can be expressed concisely, reuse existing software components, and perform better than the current state of the art in terms of coverage over time and number of objects covered overall.

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Section: Problems Related To Omokcmentioning

confidence: 99%

A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems

Pianini

Pettinari

Casadei

et al. 2022

ACM Trans. Auton. Adapt. Syst.

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show abstract

“…×2, ×3, ×4), then convolve the downsampled images with the motion blur kernels k for training (see supplementary material), and also add Gaussian noises with 1%, 2%, 3%, and 5% noise standard deviation to generate LR image patches. Instead of training a customized model for blur kernels with fixed dimension and non-blind noise levels, we uniformly sample kernel sizes from a set [11,13,15,17,19,21,23,27,29,31] and noise levels from an interval [1%, 2%, 3%, 5%] 1 , which helps to learn a more versatile model to handle diverse data.…”

Section: Training Datasetmentioning

confidence: 99%

“…SISR problem is a classical problem with various practical applications [33] in satellite imaging, medical imaging, astronomy, microscopy imaging, seismology, remote sensing, surveillance, biometric, etc. In the surveillance field and in particular in case distributed cameras networks [27], the possibility to transfer low resolution images is a very important feature that allows to share like Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.…”

Section: Introductionmentioning

confidence: 99%

Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations

Umer

Foresti

Micheloni

2019

Proceedings of the 13th International Conference on Distributed Smart Cameras

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Single Image Super-Resolution (SISR) aims to generate a highresolution (HR) image of a given low-resolution (LR) image. The most of existing convolutional neural network (CNN) based SISR methods usually take an assumption that a LR image is only bicubicly down-sampled version of an HR image. However, the true degradation (i.e. the LR image is a bicubicly downsampled, blurred and noisy version of an HR image) of a LR image goes beyond the widely used bicubic assumption, which makes the SISR problem highly ill-posed nature of inverse problems. To address this issue, we propose a deep SISR network that works for blur kernels of different sizes, and different noise levels in an unified residual CNN-based denoiser network, which significantly improves a practical CNNbased super-resolver for real applications. Extensive experimental results on synthetic LR datasets and real images demonstrate that our proposed method not only can produce better results on more realistic degradation but also computational efficient to practical SISR applications. KEYWORDSsuper-resolution, convolutional neural network, realistic degradations, computational efficient.

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References Part 5

Hentea¹

2021

Building an Effective Security Program for Distributed Energy Resources and Systems

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Self-Reconfigurable Smart Camera Networks

Abstract: Camera networks that reconfigure while performing multiple tasks have unique requirements, such as concurrent task allocation with limited resources, the sharing of data among fields of view across the network, and coordination among heterogeneous devices.

Cited by 53 publications

References 15 publications

A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems

A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems

Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations

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