PTZ-Surveillance coverage based on artificial intelligence for smart cities

Eldrandaly, Khalid A.; Abdel‐Basset, Mohamed; Abdel-Fatah, Laila

doi:10.1016/j.ijinfomgt.2019.04.017

Cited by 30 publications

(13 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advanced AI surveillance technologies, enabled by motion detection, predictive analytics, drones, and other autonomous devices, can be used to monitor urban areas, recognize threats, such as crime [72,93,100,148], fraud [109], accidents, and fire [101,123,160]. On a broader scale, AI can be used to monitor communication networks and recognize potential terrorist threats, trafficking, crime syndicates, and other illegal behavior [100].…”

Section: Ai In the Governance Dimension Of Smart Citiesmentioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

View full text Add to dashboard Cite

Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

show abstract

Section: Ai In the Governance Dimension Of Smart Citiesmentioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In their study, they have proven that minimum-number full-view coverage is a NP-hard problem and only suboptimal solution can be found. Eldrandaly et al introduce an artificial intelligence algorithm to adjust the orientations of cameras for increasing the coverage of the PTZ surveillance system [11]. Sung et al propose a distributed greedy algorithm based on Voronoi diagram to adjust the orientations of sensors to improve the effective field coverage of directional sensor networks [12].…”

Section: Related Workmentioning

confidence: 99%

Coverage Enhance in Boundary Deployed Camera Sensor Networks for Airport Surface Surveillance

Wang

Han

2021

IEEE Access

View full text Add to dashboard Cite

Unlike the conventional coverage issue in camera sensor networks, the airport surface surveillance brings new challenges for achieving the desirable coverage. One of the biggest challenges comes from the deployment of camera sensors, i.e. due to the consideration of safety, the cameras are always installed on the land-side facilities in airports. In this paper, we propose a coverage enhancing method by deploying additional cameras for the boundary deployed heterogeneous camera networks. We convert the area coverage problem to maximize the barrier coverage in the area of interest by a boundary deployed camera network. The proposed method is divided into three phases: detecting the uncovered gaps in barriers; finding the optimal positions for deploying additional cameras; and optimizing the orientation of the additional cameras to cover the uncovered gaps. The simulation results confirm that our algorithm has better performance in coverage improvement and additional camera usage compared with other related methods.

show abstract

“…Therefore, users can access the server data from a PC with a web browser installed. In addition, the data stored in the database can make it more convenient and fast for managers to sort out data [21]. The database includes the following: user information table, scenic spot information table, and sensor information.…”

Section: Design Of the Tourist Information Collection Modulementioning

confidence: 99%

Wireless Sensor Networks Assist Intelligent Tourism Traffic Navigation and Tourist Positioning

Sun

2021

Journal of Sensors

View full text Add to dashboard Cite

With the booming development of tourism, more and more tourists visit scenic spots; followed by dense personnel in scenic spots, tourists’ comfort is not high. This paper introduces the structure diagram, function structure diagram, and database design of an intelligent tourism navigation system based on a wireless sensor network in detail and explains each function module of an intelligent tourism system based on a wireless sensor network in detail. Through the wireless sensor network to obtain the positioning data of tourists in tourist attractions, this article realizes the intelligent traffic navigation in the intelligent tourism navigation system and finally realizes the diversion of tourists. An intelligent travel navigation system based on the wireless sensor network is implemented on Android. Finally, the intelligent travel navigation system based on the wireless sensor network is tested. The results show that the system can meet the real-time demand, the system positioning accuracy is high, and the system can effectively realize the flow analysis and public security warning.

show abstract

PTZ-Surveillance coverage based on artificial intelligence for smart cities

Cited by 30 publications

References 42 publications

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Coverage Enhance in Boundary Deployed Camera Sensor Networks for Airport Surface Surveillance

Wireless Sensor Networks Assist Intelligent Tourism Traffic Navigation and Tourist Positioning

Contact Info

Product

Resources

About