An Advanced Framework for Traffic Parameters Estimation from UAV Video

Currently unmanned aerial vehicle (UAV) is at the heart of traffic sensing research due to its advantages such as low cost, high flexibility, and wide view range over traditional traffic sensing technologies. It opens up new opportunities for intelligent transportation systems by supporting efficient and reliable traffic monitoring and decision making. Under such context, an increasing trend of emerging research on UAV-based traffic detection can be observed. Recent studies have already achieved great progress on the extraction of aggregated macroscopic traffic parameters from UAV videos, however, there is still a wide gap between the state-of-the-art methods and a complete framework that can automatically estimate both microscopic traffic parameters and lane-level macroscopic traffic parameters at the same time. In this paper, an advanced framework is proposed to fill this gap by addressing several key challenges. This framework is composed of three functional modules: core functional module, data storing module, and traffic parameters estimation module. The core functional module consists of two sub-modules, which are a new method for lane detection and an exclusive method for multiple vehicle tracking in UAV video. The integration of the two sub-modules enables the collection of various raw traffic data, which are then organized and stored in the data storing module for further processing. The traffic parameters estimation module is designed to calculate the macroscopic and microscopic traffic parameters, and to analyze individual vehicle behaviors. Experiments on real-world UAV video data and thorough analyses on the results demonstrate the promising performances of the proposed framework.

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  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABJ50 Standing Committee on Information Systems and Technology.
  • Corporate Authors:

    Transportation Research Board

    ,    
  • Authors:
    • Ke, Ruimin
    • Feng, Shuo
    • Cui, Zhiyong
    • Wang, Yinhai
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01697656
  • Record Type: Publication
  • Report/Paper Numbers: 19-02564
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:33AM