Latest revision as of 23:47, 1 February 2021

Abstract

In urban environments there are daily issues of traffic congestion which city authorities need to address. Realtime analysis of traffic flow information is crucial for efficiently managing urban traffic. This paper aims to conduct traffic analysis using UAV-based videos and deep learning techniques. The road traffic video is collected by using a position-fixed UAV. The most recent deep learning methods are applied to identify the moving objects in videos. The relevant mobility metrics are calculated to conduct traffic analysis and measure the consequences of traffic congestion. The proposed approach is validated with the manual analysis results and the visualization results. The traffic analysis process is real-time in terms of the pre-trained model used.

Original document

The different versions of the original document can be found in:

• http://e-space.mmu.ac.uk/624465

• https://e-space.mmu.ac.uk/624465/1/IWT4S_Submission_HZhang.pdf

• http://xplorestaging.ieee.org/ielx7/8894828/8909819/08909879.pdf?arnumber=8909879,

http://dx.doi.org/10.1109/avss.2019.8909879

• https://dblp.uni-trier.de/db/conf/avss/avss2019.html#ZhangLBC19,

https://research.edgehill.ac.uk/en/publications/real-time-traffic-analysis-using-deep-learning-techniques-and-uav,

https://doi.org/10.1109/AVSS.2019.8909879,

https://academic.microsoft.com/#/detail/2989687024