Extracting the fundamental diagram from aerial footage

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object , Other literature type 01 May 2020Embargo end date: 01 Jan 2020Publisher:IEEEJournal:2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)Funded by:EC | KIOS CoE

Authors: Rafael Makrigiorgis; Panayiotis Kolios; Stelios Timotheou; Theocharis Theocharides; Christos G. Panayiotou;

doi: 10.1109/vtc2020-spring48590.2020.9128534 , 10.48550/arxiv.2007.03227

arXiv: 2007.03227

Extracting the fundamental diagram from aerial footage

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Abstract

Efficient traffic monitoring is playing a fundamental role in successfully tackling congestion in transportation networks. Congestion is strongly correlated with two measurable characteristics, the demand and the network density that impact the overall system behavior. At large, this system behavior is characterized through the fundamental diagram of a road segment, a region or the network. In this paper we devise an innovative way to obtain the fundamental diagram through aerial footage obtained from drone platforms. The derived methodology consists of 3 phases: vehicle detection, vehicle tracking and traffic state estimation. We elaborate on the algorithms developed for each of the 3 phases and demonstrate the applicability of the results in a real-world setting.

5 pages, 7 figures, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

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Keywords

FOS: Computer and information sciences, Computer Vision, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Aerial footage, IEEE, Deep Learning, FOS: Electrical engineering, electronic engineering, information engineering, Vehicle detection, Data mining, KIOS, Traffic Monitoring

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