Visual Sensor Placement Based on Risk Maps

Altahir, Altahir A.; Asirvadam, Vijanth S.; Hamid, Nor Hisham; Sebastian, Patrick; Hassan, Mohamed Abul; Saad, Nordin; Ibrahim, Rosdiazli; Dass, Sarat C.

doi:10.1109/tim.2019.2927650

Cited by 16 publications

(14 citation statements)

References 26 publications

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“…We also achieve the lowest scores in terms of PCM because cameras have no incentive in exploring new areas. Experiments (7)(8)(9) are conducted using a directional crowd (Figure 4b). When the network focuses only on observation in (9), it obtains the best results in terms of PCM and the worst one in terms of global coverage GCM.…”

Section: Quantitative Resultsmentioning

confidence: 99%

“…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%

“…Recently, Altahir et al [ 7 ] solved the camera placement problem with predefined risk maps which have an higher priority to be covered. In [ 30 ], a distributed Particle Swarm Optimisation (PSO) is employed to maximise the geometric coverage of the scene.…”

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 ]. In such scenarios, a camera network must be able to record local events as well as to ensure a global coverage of the area of interest [ 8 ].…”

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: Quantitative Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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“…The placement was based on a 2D risk map in 3D space. Inversely, the sensors were placed based on 2D weighted coverage demand [ 35 ]. As a continuation of their work, they used dynamic programming as a discrete optimization for 2D generated urban layout [ 36 ].…”

Section: Related Literaturementioning

confidence: 99%

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Kovács

Bolemányi

Botzheim

2022

Sensors

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This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1×1×1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%.

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A Genetic Optimization Method for Spatial Layout of Cameras in Video Sensor Networks

Yue,

2023

2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

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Visual Sensor Placement Based on Risk Maps

Cited by 16 publications

References 26 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

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

A Genetic Optimization Method for Spatial Layout of Cameras in Video Sensor Networks

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