Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video. Issue 7 (16th April 2020)

• Record Type: Journal Article
• Authors: Ke, Ruimin; Feng, Shuo; Cui, Zhiyong; Wang, Yinhai
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 7(2020)
• Page Start: 724
• Page End: 734
• Publication Date: 2020-04-16
• Subjects: autonomous aerial vehicles -- road vehicles -- road traffic -- object detection -- parameter estimation -- traffic engineering computing -- video signal processing -- robot vision; advanced traffic sensing -- microscopic lane‐level macroscopic traffic parameters estimation -- modern traffic sensing research -- UAV‐based traffic sensing -- aggregated macroscopic traffic parameters -- aggregated macroscopic traffic data -- higher‐resolution traffic data -- traffic patterns -- traffic flow characteristics -- real‐world UAV video data -- advanced traffic management -- UAV video features; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0463
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Unmanned aerial vehicle (UAV) is at the heart of modern traffic sensing research due to its advantages of low cost, high flexibility, and wide view range over traditional traffic sensors. Recently, increasing efforts in UAV‐based traffic sensing have been made, and great progress has been achieved on the estimation of aggregated macroscopic traffic parameters. Compared to aggregated macroscopic traffic data, there has been extensive attention on higher‐resolution traffic data such as microscopic traffic parameters and lane‐level macroscopic traffic parameters since they can help deeply understand traffic patterns and individual vehicle behaviours. However, little existing research can automatically estimate microscopic traffic parameters and lane‐level macroscopic traffic parameters using UAV videos with a moving background. In this study, an advanced framework is proposed to bridge the gap. Specifically, three functional modules consisting of multiple processing streams and the interconnections among them are carefully designed with the consideration of UAV video features and traffic flow characteristics. Experimental results on real‐world UAV video data demonstrate promising performances of the framework in microscopic and lane‐level macroscopic traffic parameters estimation. This research pushes off the boundaries of the applicability of UAVs and has an enormous potential to support advanced traffic sensing and management.