Collection and Consolidation of Big Data for Proactive Monitoring of Critical Events at Infrastructure Facilities in an Urban Environment

Finogeev, Alexey; Parygin, Danila; Schevchenko, Sergey; Ather, Danish

doi:10.1007/978-3-030-87034-8_25

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…As technology continues to evolve, it is anticipated that more cities will adopt these systems, ushering in a new era of sustainable urban environments. [12]- [18].…”

Section: 5mentioning

confidence: 99%

Adaptive Illumination: Designing a Smart Street Lighting System for Sustainable Urban Environments

Sultonmurod Shavkatov

2023

matrix

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In the age of rapid technological advancement and increasing urbanization, the need for sustainable and efficient infrastructure has never been more pronounced. As cities grow and evolve, so do the challenges related to managing their energy consumption, safety, and environmental footprint. One of the critical components of urban infrastructure that has a direct impact on all these areas is street lighting. Traditional street lighting systems, while essential for safety and visibility, often consume excessive amounts of energy, contribute to light pollution, and require significant maintenance. This is where the concept of adaptive illumination comes into play as shown in Figure 1. Adaptive illumination refers to the design and implementation of street lighting systems that can adjust their brightness and intensity based on real-time environmental conditions and human activity. Unlike conventional systems that remain uniformly lit throughout the night, adaptive systems use sensors, data analytics, and connectivity to dynamically modify their output. This not only results in significant energy savings but also reduces light pollution and enhances the overall safety and comfort of urban residents.[1] The sustainability aspect of adaptive illumination cannot be overstated. As the world grapples with climate change and the pressing need to reduce carbon emissions, every kilowatt-hour saved has a cascading positive effect on the environment. By ensuring that street lights are only as bright as they need to be, cities can dramatically decrease their energy consumption. This is particularly relevant in the context of urban areas, which are often the largest consumers of electricity within a region.

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“…As technology continues to evolve, it is anticipated that more cities will adopt these systems, ushering in a new era of sustainable urban environments. [12]- [18].…”

Section: 5mentioning

confidence: 99%

Adaptive Illumination: Designing a Smart Street Lighting System for Sustainable Urban Environments

Sultonmurod Shavkatov

2023

matrix

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“…The objects of monitoring are the road sections, the elements of the road infrastructure, the vehicles, and the pedestrians. The sustainable functioning of this system requires solving the problems of identifying and classifying road users in order to predict the likelihood of traffic accidents and prevent emergency situations [1,2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

et al. 2022

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The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%.

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Management of Information from Surveillance Cameras at the Infrastructure Facility

Parygin

Gurtyakov

Finogeev

et al. 2022

Intelligent Systems Reference Library

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Collection and Consolidation of Big Data for Proactive Monitoring of Critical Events at Infrastructure Facilities in an Urban Environment

Cited by 7 publications

References 25 publications

Adaptive Illumination: Designing a Smart Street Lighting System for Sustainable Urban Environments

Adaptive Illumination: Designing a Smart Street Lighting System for Sustainable Urban Environments

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

Management of Information from Surveillance Cameras at the Infrastructure Facility

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