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.
Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video
IET Intelligent Transport Systems ; 14 , 7 ; 724-734
2020-07-01
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
traffic flow characteristics , UAV video features , higher‐resolution traffic data , advanced traffic sensing , object detection , aggregated macroscopic traffic data , real‐world UAV video data , road traffic , microscopic lane‐level macroscopic traffic parameters estimation , advanced traffic management , traffic engineering computing , traffic patterns , autonomous aerial vehicles , video signal processing , aggregated macroscopic traffic parameters , parameter estimation , robot vision , UAV‐based traffic sensing , road vehicles , modern traffic sensing research
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