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Neural Video Processing and Streaming for Real-time Traffic Monitoring

Project description

AI for real-time traffic monitoring

Rapid urbanisation and the permanent increase in vehicle numbers raised concerns about road safety. As a result, real-time traffic monitoring systems have been installed to assist operators in traffic control and emergency situations. The EU-funded VISIONS project will develop a real-time traffic monitoring system with high-quality video transmission in smart cities based on AI methods in video processing and video streaming. The characteristics of human visual systems will be denoted on video quality allocation to restrict the necessary range for video transmission. An advanced method based on deep neural networks will be deployed to permit video rendering and streaming at lower resolutions, and a novel bitrate adaptation system based on reinforcement learning will be planned to guarantee quality-of-experience.

Objective

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.

Coordinator

THE UNIVERSITY OF EXETER
Net EU contribution
€ 224 933,76
Address
THE QUEEN'S DRIVE NORTHCOTE HOUSE
EX4 4QJ Exeter
United Kingdom

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Region
South West (England) Devon Devon CC
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 224 933,76