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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2017  >  17(1): 149-158
NIE Yin-yu, TANG Zhao, CHANG Jian, LIU Feng-jia, ZHANG Jian-jun. 3D reconstruction of train accident scene based on monocular image[J]. Journal of Traffic and Transportation Engineering, 2017, 17(1): 149-158.
Citation: NIE Yin-yu, TANG Zhao, CHANG Jian, LIU Feng-jia, ZHANG Jian-jun. 3D reconstruction of train accident scene based on monocular image[J]. Journal of Traffic and Transportation Engineering, 2017, 17(1): 149-158.
shu

3D reconstruction of train accident scene based on monocular image

  • 1.

    State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China

  • 2.

    National Computer Animation Centre, Bournemouth University, Bournemouth BH12 5BB, Dorset

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  • Author Bio:

    NIE Yin-yu(1992-), male, graduatestudent, +86-28-87634042, nieyinyu0731@163.com

    TANG Zhao(1979-), male, lecturer, PhD, +86-28-87634042, tangzhao@swjtu.edu.cn

  • Received Date: 2016-08-01
  • Publish Date: 2017-02-25
    Abstract
  • To help with making an emergency rescue plan for train accidents, a rapid 3D reconstruction method of train accident scene based on a monocular image was proposed.Taking two camera projection models for different application scenarios into consideration, the SIFT algorithm was introduced to extract and match image feature with the CAD model of an accident train.Geometric constraints between vehicles were provided to transform the 3D reconstruction to solving a nonlinear least square problem with constraints, by which the position and pose of accident subjects were reduced at last.To quantitatively and qualitatively verify the calculation performance of the method, the mimicked train accident scene and the real train accident scene were respectively used to carry out 3D reconstruction.The precise finite camera projection model was applied in the mimicked train accident scene to carry out offline calibration, and the stable pin-hole model was adopted in the real train accident scene to carry out auto calibration.Analysisresult shows that through quantitative analysis of mimicked scene, the maximal and average relative error of 8 nodes for measurement in reconstructing two vehicles are 4.54% and 1.85% respectively.Through qualitative analysis of real scene, the 3D reduction of position and pose for vehicles can also be realized by combining the topographic information correction.The whole accident environmental panorama reduces visually with the help of 3D visualization engine.

     

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