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Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video

Year: 2019

Type: conference-paper

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.

Source: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

DOI: 10.1109/avss.2019.8909879

Authors: Huaizhong Zhang, Mark Liptrott, Nik Bessis, Jianquan Cheng

Institutions: Edge Hill University, Manchester Metropolitan University

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W2989687024