On the optimal placement of multiple visual sensors

Hörster, Eva; Lienhart, Rainer

doi:10.1145/1178782.1178800

Cited by 154 publications

(35 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As observed in [7], multimedia sensor networks enhance traditional surveillance systems by enlarging, enhancing, and enabling multi-resolution views. The investigation of coverage problems for static visual sensor networks is conducted in [5] [13].…”

mentioning

confidence: 99%

Distributed Coverage Games for Energy-Aware Mobile Sensor Networks

Zhu¹,

Martínez

2013

SIAM J. Control Optim.

151

168

View full text Add to dashboard Cite

Motivated by current challenges in data-intensive sensor networks, we formulate a coverage optimization problem for mobile visual sensors as a (constrained) repeated multi-player game. Each visual sensor tries to optimize its own coverage while minimizing the processing cost. We present two distributed learning algorithms where each sensor only remembers its own utility values and actions played during the last plays. These algorithms are proven to be convergent in probability to the set of (constrained) Nash equilibria and global optima of certain coverage performance metric, respectively.

show abstract

mentioning

confidence: 99%

Distributed Coverage Games for Energy-Aware Mobile Sensor Networks

Zhu¹,

Martínez

2013

SIAM J. Control Optim.

151

168

View full text Add to dashboard Cite

show abstract

“…The angular view or field of view (FoV) and limited range for each guard, however, makes the area coverage problem with cameras more complex. Even after discretization of space and camera poses, potential solutions remain unscalable [63]. Therefore, a number of heuristics using angular sweeping for camera pans have been proposed, which proved to have performance close to optimal, although still require high computational complexity.…”

Section: ) Fixed Deploymentmentioning

confidence: 99%

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Shakeri

Al-Garadi

Badawy

et al. 2019

IEEE Commun. Surv. Tutorials

205

100

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. M 2 respectively. However, as shown in Fig.1, both technologies provide two extremes for surveillance with respect to accuracy versus intrusive deployment and maintenance. SRS provides slow large-scale monitoring, while CSNs require widespread deployment and constant human intervention for setup and maintenance, which may not be feasible particularly in harsh environments. On the one hand, SRS cannot be used for finegrain monitoring, such as monitoring of structural objects, with high accuracy, which limits its usage in many applications that require close monitoring and context detection [5]. On the other hand, traditional sensor networks (SNs) are spreading in countless number of Internet of Things (IoT) applications to provide automatic monitoring of objects, humans, and/or infrastructure. SNs, however, require permanent deployment of large magnitude of localized sensors, intrusively to the environment in many cases, and also require constant human intervention to manage and maintain. The proliferation of lowcost UAVs [6] is emerging to provide innovative ways of low altitude sensing with zero-deployment (i.e., no fixed deployment requirements) that can fundamentally change the way such applications are designed.Similar to surveillance applications, UAV technologies have progressed significantly in several applications in the past decade, making commercial and civil applications feasible and compelling. As it occurs with many disruptive technolo...

show abstract

“…The distribution and optimization of camera network is also known as camera network deployment (Debaque, Jedidi, and Prevost 2009), placement (Zhao et al 2011;Hörster and Lienhart 2006), coverage-enhancing (Ma, Zhang, and Ming 2009) and planning (Zhao and Cheung 2009). The most common goal for camera network deployment optimization problem is to meet the coverage requirements with the least number of cameras.…”

Section: Related Workmentioning

confidence: 99%

Deployment optimization for camera network coupling with geographic environment

Wang

Liu

et al. 2018

Annals of GIS

View full text Add to dashboard Cite

Surveillance cameras are widely deployed to monitor the geographical environment, traffic condition, public security, so on. Optimal deployment of surveillance cameras is an effective way to promote their validity and utilization. Because of the ignorance of geographic environment in major existing researches, a new optimization method which considers the constraining relationships between cameras and geographic environment is proposed. Firstly, coverage models for single camera and camera network which consider the occlusions in geographic environment are discussed. Secondly, the objective of optimization is put forward, which is to employ the minimum cameras to monitor the specific areas. Thirdly, to achieve the objective, the heuristic search algorithm is introduced. The deployable camera pose and location, and target area are discretized to promote the efficiency. Consequently, the deployment optimization problem is abstracted into a combinatorial optimization problem. Then optimization model including the graph structure, cost function and heuristic function for the algorithm is built to meet the requirement of the heuristic search algorithm, and its correctness is also strictly proofed. Finally, experiment in real city area with occlusions (such as building) validates the usability of our method. The sampling steps of camera pose and location and the split size of target area for discrete process are two important factors to influence the degree of accuracy and efficiency. The results obtained by our method can provide decision support for optimal camera deployment in real geographic environment, as well as provide reference for other environmental monitoring sensor deployment optimization tasks. ARTICLE HISTORY

show abstract

On the optimal placement of multiple visual sensors

Cited by 154 publications

References 9 publications

Distributed Coverage Games for Energy-Aware Mobile Sensor Networks

Distributed Coverage Games for Energy-Aware Mobile Sensor Networks

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Deployment optimization for camera network coupling with geographic environment

Contact Info

Product

Resources

About