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Neural Video Processing and Streaming for Real time Traffic Monitoring

With the rapid development of urbanization and continuous increase of vehicles on roadways, Intelligent Transportation Systems (ITS) play a key role in revolutionizing the way people commute. To make our cities safer and smarter,... ver más

31/07/2023

UNEXE

225K€

Presupuesto del proyecto: 225K€

Líder del proyecto

THE UNIVERSITY OF EXETER No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-07-31

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

0% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UNEXE

224.93K€ | Lider

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Duración del proyecto: 39 meses Fecha Inicio: 2020-04-07
Fecha Fin: 2023-07-31

Líder del proyecto

THE UNIVERSITY OF EXETER No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 225K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto With the rapid development of urbanization and continuous increase of vehicles on roadways, Intelligent Transportation Systems (ITS) play a key role in revolutionizing the way people commute. To make our cities safer and smarter, real-time traffic monitoring systems are deployed to help operators with observing traffic flows and identifying emergency situations. This project aims to achieve real-time traffic monitoring with high-quality video transmission in smart cities, leveraging the emerging Artificial Intelligence methods in video processing and video streaming. Firstly, the features of human visual systems will be referred on video quality allocation to reduce the required bandwidth for video transmission. Next, an innovative method for end-to-end video processing based on Deep Neural Networks will be developed to allow the video rendering and streaming at a lower resolution and also restore/improve the quality at the user ends. Finally, a new bitrate adaption scheme based on Reinforcement Learning will be designed to accommodate the unexpected network dynamics, guaranteeing the Quality-of-Experience to be perceived by users. The expected outcome can promote safer and more efficient travel for millions of users in Europe and billions of users all over the world. Moreover, the results of this project can be used in other multimedia applications, such as cloud virtual reality, distance education, smart transportation, and healthcare where video processing and video streaming are needed. To broaden the fellow’s knowledge horizon, a series of research, training, and knowledge transfer activities are planned. The new knowledge and skills imparted in these activities will further promote his academic portfolio and significantly enhance his career prosperity. The project will also play a solid foundation for the long-term and wide-range collaborations and eventually lead to more extensive impact of project results, from which both EU and China will benefit.

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