Accurate Traffic Parameter Extraction from Aerial Videos with Multi-Dimensional Camera Movements

Unmanned Aerial Vehicle (UAV) is becoming increasingly popular in traffic monitoring due to its low cost, wide view coverage, and rapid deployment. Extracting traffic parameters from UAV videos can provide important information which benefits traffic flow analysis and control. Most of previous studies focused on extracting aggregated traffic parameter planar movement of UAV cameras. Little attention has been paid on the videos recorded with multi-dimensional camera movement. To address this issue, this paper proposes a novel framework for traffic parameter extraction with UAV camera moving at different dimensions. The authors first employ a temporally robust global motion compensation (TRGMC) model to compensate UAV camera movements and obtain stabilized background. Then, the kernelized correlation filter (KCF) is applied to track vehicles fast and accurately. After that, the authors employ the Hough line detection to find out reference markings and map the image length to physical length. Finally, microscopic traffic parameters including individual vehicle speed, time headway and space headway in a traffic stream are estimated using outputs from previous steps. The experimental results show that the proposed method achieves an accuracy of about 94.60% and 93.94% in estimating vehicle speed and headway, respectively.

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

    Transportation Research Board

  • Authors:
    • Li, Zhibin
    • Chen, Xinqiang
    • Ling, Lei
    • Wu, Huafeng
    • Zhou, Wenzhu
    • Qi, Cong
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 7p

Subject/Index Terms

Filing Info

  • Accession Number: 01697659
  • Record Type: Publication
  • Report/Paper Numbers: 19-02817
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:33AM