Real-time monitoring of traffic parameters

Khazukov, Kirill; Shepelev, Vladimir; Karpeta, Tatiana; Shabiev, Salavat; Slobodin, Ivan; Charbadze, Irakli

doi:10.1186/s40537-020-00358-x

Cited by 40 publications

(7 citation statements)

References 30 publications

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“…This model demonstrates the capacity to dynamically enhance the signal cycle at various intersections, ensuring a remarkably adaptable and responsive approach. Utilizing inputs from camera images positioned strategically along the roadway, our proposed fuzzy logic controller effectively regulates traffic light operations, a feature further supported by the well-documented probability distribution illustrating the flexibility of the fuzzy logic system, thus yielding promising outcomes in simulations (Khazukov, 2020). This controller model enables the implementation of tailored green timing sequences, contingent upon the vehicular density at each intersection.…”

Section: Neural Networkmentioning

confidence: 90%

Venturing into the Age of AI: Insights and Perspectives

2023

FORUM A+P

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ObjectivesThe aims of the Workshop in Multimedia Design were to combine the content of the multimedia with applications connected to product branding. The theoretical aspects were presented, while at the same time, a series of exercises were performed and presented within the class. The participants worked in teams and at the end of every exercise, they were all actively present the projects' outcomes. The rest of the participants and the curators asked questions in order to clarify issues that came up during the presentations. Additionally, a series of digital tools was presented based on real case scenarios and thus the participants were exposed to knowledges and experiences, when using a multimedia toolkit, with an increased number of tools, from different points of view i.e. text, audio, sketching, vector design, image processing, animation, video, artificial intelligence based media creations.

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Section: Neural Networkmentioning

confidence: 90%

Venturing into the Age of AI: Insights and Perspectives

2023

FORUM A+P

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“…A checkerboard overlay was used to calibrate and eliminate the radial camera distortion. A perspective transformation matrix based on four reference points was used to determine the geographical coordinates of vehicles [40]. It converts the screen coordinates of an object into its longitude and latitude at the intersection.…”

Section: Intelligent Vision and Initial Data Generationmentioning

confidence: 99%

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Shepelev,

Glushkov,

Slobodin

et al. 2023

Mathematics

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This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold.

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“…The purpose of the counting task is to predict the number of objects existing in an image or video. Object counting is a key part of the workflow in many major CV applications, such as traffic monitoring (Khazukov et al, 2020;Zhang et al, 2017). In the context of marine application and fish monitoring, counting may be used to map distinct species and monitor fish populations for effective conservation.…”

Section: Countingmentioning

confidence: 99%

Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey

Saleh¹,

Sheaves²,

Jerry³

et al. 2022

Preprint

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Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserving the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localization, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring.

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Real-time monitoring of traffic parameters

Cited by 40 publications

References 30 publications

Venturing into the Age of AI: Insights and Perspectives

Venturing into the Age of AI: Insights and Perspectives

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey

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