Global Coverage Maximization in PTZ-Camera Networks Based on Visual Quality Assessment

Konda, Krishna Reddy; Conci, Nicola; Natale, Francesco G. B. De

doi:10.1109/jsen.2016.2584179

Cited by 28 publications

(16 citation statements)

References 23 publications

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“…The QoV is expected to be employed to measure the quality of retrieved data for the monitoring requirements of the applications and thus it depends on the particularities of each considered scenario. Coverage assessment and improvement were also performed in [25], which optimized the orientation of cameras for indoor monitoring. Metrics for coverage of targets and areas have also been proposed in the last years, being discussed and compared in some works [15], [26].…”

Section: Related Workmentioning

confidence: 99%

Modelling Coverage Failures Caused by Mobile Obstacles for the Selection of Faultless Visual Nodes in Wireless Sensor Networks

et al. 2020

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Wireless sensor networks comprising nodes equipped with cameras have become common in many scenarios, providing valuable visual data for some relevant services such as localization, tracking, patterns identification and emergencies detection. In this context, algorithms and optimization approaches have been designed to perform different types of quality assessment or performance enhancement tasks, addressing challenging issues such as networking, compression, availability, reliability, security, energy efficiency and virtually any subject related to the operational challenges of those networks. However, the dynamics of coverage failures have not been properly modelled in visual sensor networks, resulting in unrealistic perceptions when optimizing or assessing quality in most visual sensing scenarios. Particularly, the Field of View of visual sensors will be affected by occlusion caused by obstacles in the monitored field, which may turn such sensors inadequate for the expected monitoring services of the considered network. Therefore, this article proposes a mathematical model to assess occlusion caused by mobile obstacles such as vehicles on a road or forklifts in an industrial plant, aiming at the selection of the visual sensor nodes that will not have their coverage significantly restricted by those obstacles. Doing so, the proposed model can be exploited by any optimization or quality assessment approach in wireless visual sensor networks, providing a preprocessing method when selecting visual nodes. INDEX TERMS Wireless sensor networks, visual sensing, sensors selection, coverage failures, mathematical modelling.

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Section: Related Workmentioning

confidence: 99%

Modelling Coverage Failures Caused by Mobile Obstacles for the Selection of Faultless Visual Nodes in Wireless Sensor Networks

et al. 2020

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“…In contrast, we do not consider covering objects in this work, but are purely interested in geometric coverage of an area. Konda et al [14] present a planning algorithm, maximising coverage for the entire network based on visual quality assessment. For this centralised approach they employ a Particle Swarm Optimizer.…”

Section: Related Workmentioning

confidence: 99%

Centralised, Decentralised, and Self-Organised Coverage Maximisation in Smart Camera Networks

Esterle

2017

2017 IEEE 11th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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Abstract-When maximising the coverage of a camera network, current approaches rely on a central approach and rarely consider the decentralised or even self-organised potential. In this paper, we study the performance of decentralised and selforganised approaches in comparison to centralised ones in terms of geometric coverage maximisation. We present a decentralised and self-organised algorithm to maximise coverage in a camera network using a Particle Swarm Optimiser (PSO) and compare them to a centralised version of PSO. Additionally, we present a decentralised and self-organised version of ARES, a centralised approximation algorithm for optimal plans combining PSO, Importance Splitting, and an adaptive receding horizons at its core. We first show the benefits of ARES over using PSO as a single, centralised optimisation algorithm when used before deployment time. Second, since cameras are not able to change instantaneously, we investigate gradual adaptation of individual cameras during runtime. Third, we compare achieved geometrical coverage of our decentralised approximation algorithm against the centralised version of ARES. Finally, we study the benefits of a self-organised version of PSO and ARES, allowing the system to improve its coverage over time. This allows the system to deal with quickly unfolding situations.

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“…With their high mobility and zoom capability, pan-tiltzoom (PTZ) cameras have become increasingly popular in monitoring public areas [1], [2], [3]. Omnidirectional cameras are promising candidates for monitoring multiple latent activities in the area of interest [4].…”

Section: Introductionmentioning

confidence: 99%

Panoramic background modeling for PTZ cameras with competitive learning neural networks

Thurnhofer‐Hemsi

López‐Rubio

Domínguez

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

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Abstract-The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed method is effective and suitable to use for real-time video surveillance applications.

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Global Coverage Maximization in PTZ-Camera Networks Based on Visual Quality Assessment

Cited by 28 publications

References 23 publications

Modelling Coverage Failures Caused by Mobile Obstacles for the Selection of Faultless Visual Nodes in Wireless Sensor Networks

Modelling Coverage Failures Caused by Mobile Obstacles for the Selection of Faultless Visual Nodes in Wireless Sensor Networks

Centralised, Decentralised, and Self-Organised Coverage Maximisation in Smart Camera Networks

Panoramic background modeling for PTZ cameras with competitive learning neural networks

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