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Calculating the Truck’s Box Volume with a Single Image Under the Circle Projection and Vanishing Points Constraint

  • Wei Sun
  • Wei Lu
  • Chun-yu Zhao
  • Bao-long Guo
  • Da-jian Li
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 109)

Abstract

To concise the truck’s box volume measuring system, some techniques such as calculation of camera intrinsic parameters, estimation of truck’s position and orientation, and calculation the vertex coordinates on the truck’s box are discussed in details. A novel method based on one single image is proposed under the constraint information of orthogonal vanishing points and circle projection priori. Firstly, three orthogonal vanishing points are detected, the intrinsic parameters of the camera are calculated based on the vanishing points. Secondly, the world coordinate system is established at the center of one of the truck’s wheels the truck’s position and orientation related to the camera’s plane and the external facade equation of truck’s box can be figured out with the camera’s intrinsic parameters and the prior of wheel’s single circular projection. Finally, the vertex coordinates of the truck’s box are calculated based on the external facade equation and matrix equation of projection algorithm. Then the volume of truck’s box can be calculated. Experimental results show that the error of the truck’s box volume is within 5%. The proposed approach is more effective and lower cost than other state of art methods, it is a competitive approach for real-time measurement of truck’s box volume.

Keywords

Single image Vanishing point Circle projection Posture calculation Volume measurement 

Notes

Acknowledgements

This work was supported by National Nature Science Foundation of China (NSFC) under Grants 61671356, 61201290.

References

  1. 1.
    Scott, W.R., Roth, G.: View planning for automated three-dimensional object reconstruction and inspection. ACM Comput. Surv. 35(1), 64–96 (2003)CrossRefGoogle Scholar
  2. 2.
    Quan, Y., Li, S., Mai, Q.: On-machine 3D measurement of workpiece dimensions based on binocular vision. Opt Precis. Eng. 21(4), 1054–1061 (2013)CrossRefGoogle Scholar
  3. 3.
    Sun, W., Chen, L., Hu, B., et al.: Binocular vision-based position determination algorithm and system. In: 2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM), 2012, pp. 170–173Google Scholar
  4. 4.
    Eisert, P., Steinbach, E., Girod, B.: Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views. IEEE Trans. Circ. Syst. Video Technol. 10(2), 261–277 (2000)CrossRefGoogle Scholar
  5. 5.
    Henry, P., Krainin, M., Herbst, E., et al.: Using Kinect-style depth cameras for dense 3D modeling of indoor environments. Int. J. Robot. Res. 31(5), 647–663 (2012)CrossRefGoogle Scholar
  6. 6.
    Liu, X., Xu, H.R., Hu, Z.Y.: GPU based fast 3D-object modeling with kinect. Acta Automat. Rocchini, C., Cignoni, P., Montani, C., et al.: A low cost 3D scanner based on structured light. Comput. Graph. Forum 20(3), 299–308 (2001)Google Scholar
  7. 7.
    Kholgade, N., et al.: 3D object manipulation in a single photograph using stock 3D models. ACM Trans. Graph. (TOG) 33(4), 127 (2014)CrossRefGoogle Scholar
  8. 8.
    Lei, Z., Ke-jun, X., Rui, Z., et al.: Improvement of position and orientation measurement algorithm of monocular vision based on circle features. J. Hefei Univ. Technol. 32(11), 1669–1673 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wei Sun
    • 1
  • Wei Lu
    • 1
  • Chun-yu Zhao
    • 1
  • Bao-long Guo
    • 1
  • Da-jian Li
    • 2
  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anChina
  2. 2.The 365 InstituteNorthwest Polytechnical UniversityXi’anChina

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