Optimal configuration of PTZ camera networks based on visual quality assessment and coverage maximization

Konda, Krishna Reddy; Conci, Nicola

doi:10.1109/icdsc.2013.6778202

Cited by 20 publications

(18 citation statements)

References 13 publications

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“…Experiments (1)(2)(3)(4)(5)(6) refer to a uniformly distributed crowd, experiments (7)(8)(9) refer to a crowd with directional motion properties. Table 2 summarises the results obtained using our split priority approach.…”

Section: Quantitative Resultsmentioning

confidence: 99%

“…Cooperative video surveillance research has been developed to drastically reduce human supervision [17][18][19]. This is implemented by allowing cooperative cameras to share real-time information among them in order to capture events and to guarantee global coverage of the area of interest [1][2][3]. When observing a crowded scenario, the state of the scene evolves dynamically and the camera network should be able to reconfigure and cover events as they happen.…”

Section: Related Workmentioning

confidence: 99%

“…Camera networks for surveillance applications play a key role to ensure safety of public gatherings [ 1 , 2 , 3 , 4 ]. Security applications in crowded scenarios have to deal with a variety of factors which can lead to critical situations [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance

Bisagno

Xamin

Natale

et al. 2020

Sensors

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Crowd surveillance plays a key role to ensure safety and security in public areas. Surveillance systems traditionally rely on fixed camera networks, which suffer from limitations, as coverage of the monitored area, video resolution and analytic performance. On the other hand, a smart camera network provides the ability to reconfigure the sensing infrastructure by incorporating active devices such as pan-tilt-zoom (PTZ) cameras and UAV-based cameras, thus enabling the network to adapt over time to changes in the scene. We propose a new decentralised approach for network reconfiguration, where each camera dynamically adapts its parameters and position to optimise scene coverage. Two policies for decentralised camera reconfiguration are presented: a greedy approach and a reinforcement learning approach. In both cases, cameras are able to locally control the state of their neighbourhood and dynamically adjust their position and PTZ parameters. When crowds are present, the network balances between global coverage of the entire scene and high resolution for the crowded areas. We evaluate our approach in a simulated environment monitored with fixed, PTZ and UAV-based cameras.

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Section: Quantitative Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance

Bisagno

Xamin

Natale

et al. 2020

Sensors

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“…With this in mind, Morsly et al [33] define the best interceptor placement problem, for which cameras are set up to maximise the efficiency of human agents, should an intruder be detected. For a variant, Konda and Conci [24] consider the possibility of network reconfigurations in scenarios involving the sudden shutting down of cameras. With similar concerns, Rebai et al [41] adjust the typical problem formulation to add so-called network protection constraints according to which a feasible solution must ensure that cameras watch each other as well as the surveillance area.…”

Section: Optimal Camera Placement For Area Coveragementioning

confidence: 99%

On the computational cost of the set cover approach for the optimal camera placement problem and possible set-cover-inspired trade-offs for surveillance infrastructure design

et al. 2021

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The NP-hard minimum set cover problem (SCP) is a very typical model to use when attempting to formalise optimal camera placement (OCP) applications. In a generic form, the OCP problem relates to the positioning of individual cameras such that the overall network is able to cover a given area while meeting a set of application-specific requirements (image quality, redundancy, ...) and optimising an objective, typically minimum cost or maximum coverage. In this paper, we focus on an application called global or persistent surveillance: camera networks which ensure full coverage of a given area. As preliminary work, an instance generation framework is proposed to create OCP instances from real-world data and solve them using existing literature. The computational cost of both the instance generation process and the solving algorithms however highlights a need for more efficient methods for decision makers to use in real-world settings. In this paper, we therefore propose to review the suitability of the approach, and more specifically to question two key elements: the impact of sampling frequencies and the importance of rigid full-coverage constraints. The results allow us to quickly provide decision makers with an overview of available solutions and trade-offs.

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“…In recent years, there have been some efforts to improve sensor models and hence increase the quality of measurements obtained from a sensor network. For instance, in [ 6 ], the deployment of wireless cameras has been carried out by considering the quality of images related to light resources, distortion phenomena in cameras, and coverage area, as well as a constraint regarding connectivity between cameras in indoor environments. However, several problems regarding the optimal deployment of sensor networks in indoor environments remain.…”

Section: Introductionmentioning

confidence: 99%

A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments

Afghantoloee

Mostafavi

2021

Sensors

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Optimal sensor network deployment in built environments for tracking, surveillance, and monitoring of dynamic phenomena is one of the most challenging issues in sensor network design and applications (e.g., people movement). Most of the current methods for sensor network deployment and optimization are empirical and they often result in important coverage gaps in the monitored areas. To overcome these limitations, several optimization methods have been proposed in the recent years. However, most of these methods oversimplify the environment and do not consider the complexity of 3D architectural nature of the built environments specially for indoor applications (e.g., indoor navigation, evacuation, etc.). In this paper, we propose a novel local optimization algorithm based on a 3D Voronoi diagram, which allows a clear definition of the proximity relations between sensors in 3D indoor environments. This proposed structure is integrated with an IndoorGML model to efficiently manage indoor environment components and their relations as well as the sensors in the network. To evaluate the proposed method, we compared our results with the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithms. The results show that the proposed method achieved 98.86% coverage which is comparable to GA and CMA-ES algorithms, while also being about six times more efficient.

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Optimal configuration of PTZ camera networks based on visual quality assessment and coverage maximization

Cited by 20 publications

References 13 publications

Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance

Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance

On the computational cost of the set cover approach for the optimal camera placement problem and possible set-cover-inspired trade-offs for surveillance infrastructure design

A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments

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