Estimating the Distance of a Human from an Object Using 3D Image Reconstruction

  • R. M. Swarna PriyaEmail author
  • C. GunavathiEmail author
  • S. L. Aarthy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)


Obstacle detection, pedestrian detection, human motion detection, etc., are the recent technologies which are booming high in the computer vision industry. These technologies play a major role in various applications like surveillance, autonomous cars, driverless vehicles, etc. Our focus is on identifying and calculating the distance of a human from an object using 3D image reconstruction. The object can be a video camera or a sensor in case of surveillance or driverless vehicle. Using this distance, intelligent decisions could be taken. This methodology can help the computer vision researchers to detect any obstacle in their region of interest and also their distance from the particular point. This work could also detect the frame in which the person or the obstacle is detected along with the distance. The above said methodology could be incorporated in traffic monitoring system for identifying or detecting the pedestrians so that accidents could be avoided.


3D reconstruction Depth estimation Obstacle detection 


  1. 1.
    Administration, N. H. T. S., Preliminary Statement of Policy Concerning Automated Vehicles, pp. 1–14, Washington, DC, (2013)Google Scholar
  2. 2.
    P. Alizadeh, M. Zeinali, A Real-Time Object Distance Measurement Using A Monocular Camera (2013) Google Scholar
  3. 3.
    N. Appiah, N. Bandaru, Obstacle detection Using Stereo Vision for Self-driving Cars (2011)Google Scholar
  4. 4.
    G. Calin, V. Roda, Real-time disparity map extraction in a dual head stereo vision system. Latin Am. Appl. Res. 37(1), 21–24 (2007)Google Scholar
  5. 5.
    Carnicelli, J. (2005). Stereo vision: measuring object distance using pixel offsetGoogle Scholar
  6. 6.
    H. Fathi, I. Brilakis, Multistep explicit stereo camera calibration approach to improve euclidean accuracy of large-scale 3D reconstruction. J. Comput. Civ. Eng. 30(1), 04014120 (2014)CrossRefGoogle Scholar
  7. 7.
    N. Goodall, Ethical decision making during automated vehicle crashes. Transp. Res. Rec.: J. Transp. Res. Board 2424, 58–65 (2014)CrossRefGoogle Scholar
  8. 8.
    C. Häne, T. Sattler, M. Pollefeys, Obstacle detection for self-driving cars using only monocular cameras and wheel odometry. Paper Presented at the Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2015)Google Scholar
  9. 9.
    A. Hohm, F. Lotz, O. Fochler, S. Lueke, H. Winner, automated driving in real traffic: from current technical approaches towards architectural perspectives. SAE Technical Paper (2014)Google Scholar
  10. 10.
    T.-S. Hsu, T.-C. Wang, An improvement stereo vision images processing for object distance measurement. Int. J. Autom. Smart Technol. 5(2), 85–90 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    C. Ilas, Electronic sensing technologies for autonomous ground vehicles: a review. Paper presented at the 2013 8th International Symposium on Advanced Topics in Electrical Engineering (ATEE) (2013)Google Scholar
  12. 12.
    K.-D. Kuhnert, M. Stommel, Fusion of stereo-camera and pmd-camera data for real-time suited precise 3d environment reconstruction. Paper presented at the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (2006)Google Scholar
  13. 13.
    G.H. Lee, F. Faundorfer, M. Pollefeys, Motion estimation for self-driving cars with a generalized camera. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)Google Scholar
  14. 14.
    C. Li, J. Wang, X. Wang, Y. Zhang, A model based path planning algorithm for self-driving cars in dynamic environment. Paper presented at the Chinese Automation Congress (CAC) (2015)Google Scholar
  15. 15.
    M.A. Mahammed, A.I. Melhum, F.A. Kochery, Object distance measurement by stereo vision. Int. J. Sci. Appl. Inf. Technol. (IJSAIT) 2(2), 05–08 (2013)Google Scholar
  16. 16.
    J. Mrovlje, D. Vrancic, Distance measuring based on stereoscopic pictures. Paper presented at the 9th International Ph.D. Workshop on Systems and Control: Young Generation Viewpoint (2008)Google Scholar
  17. 17.
    D. Murray, J.J. Little, Using real-time stereo vision for mobile robot navigation. Auton. Rob. 8(2), 161–171 (2000)CrossRefGoogle Scholar
  18. 18.
    M. Pollefeys, D. Nistér, J.-M. Frahm, A. Akbarzadeh, P. Mordohai, B. Clipp, P. Merrell, Detailed real-time urban 3d reconstruction from video. Int. J. Comput. Vis. 78(2–3), 143–167 (2008)CrossRefGoogle Scholar
  19. 19.
    M. Santoro, G. AlRegib, Y. Altunbasak, Misalignment correction for depth estimation using stereoscopic 3-d cameras. 2012 IEEE 14th International Workshop on Paper presented at the Multimedia Signal Processing (MMSP) (2012)Google Scholar
  20. 20.
    H. Walcher, Position Sensing: Angle and Distance Measurement for Engineers (Elsevier, Amsterdam, 2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia

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