Pedestrian Height Estimation and 3D Reconstruction Using Pixel-resolution Mapping Method Without Special Patterns


Extracting the three-dimensional (3D) information including location and height of a pedestrian is important for vision-based intelligent traffic monitoring systems. This paper tackles the relationship between pixels′ actual size and pixels′ spatial resolution through a new method named pixel-resolution mapping (P-RM). The proposed P-RM method derives the equations for pixels′ spatial resolutions (XY-direction) and object′s height (Z-direction) in the real world, while introducing new tilt angle and mounting height calibration methods that do not require special calibration patterns placed in the real world. Both controlled laboratory and actual world experiments were performed and reported. The tests on 3D mensuration using proposed P-RM method showed overall better than 98.7% accuracy in laboratory environments and better than 96% accuracy in real world pedestrian height estimations. The 3D reconstructed images for measured points were also determined with the proposed P-RM method which shows that the proposed method provides a general algorithm for 3D information extraction.

This is a preview of subscription content, access via your institution.


  1. [1]

    C. Setchell, E. L. Dagless. Vision-based road-traffic monitoring sensor. IEE Proceedings – Vision, Image and Signal Processing, vol. 148, no. 1, pp. 78–84, 2001. DOI:

    Article  Google Scholar 

  2. [2]

    C. C. C. Pang, S. S. Xie, S. C. Wong, K. Choi. Generalized camera calibration model for trapezoidal patterns on the road. Optical Engineering, vol. 52, no. 1, Article number 017006, 2013. DOI:

    Google Scholar 

  3. [3]

    A. Criminisi, I. Reid, A. Zisserman. Single view metrology. International Journal of Computer Vision, vol. 40, no. 2, pp. 123–148, 2000. DOI:

    Article  Google Scholar 

  4. [4]

    Z. Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, 2000. DOI:

    Article  Google Scholar 

  5. [5]

    P. K. Sinha. Image Acquisition and Preprocessing for Machine Vision Systems, Bellingham, USA: Society of PhotoOptical Instrumentation Engineers, 2012.

    Google Scholar 

  6. [6]

    L. Lee, R. Romano, G. Stein. Monitoring activities from multiple video streams: establishing a common coordinate frame. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 758–767, 2000. DOI: DOI:

    Article  Google Scholar 

  7. [7]

    S. Khan, M. Shah. Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1355–1360, 2003. DOI:

    Article  Google Scholar 

  8. [8]

    G. S. K. Fung, N. H. C. Yung, G. K. H. Pang. Camera calibration from road lane markings. Optical Engineering, vol. 42, no. 10, pp. 2967–2977, 2003. DOI:

    Article  Google Scholar 

  9. [9]

    J. Shao, S. K. Zhou, R. Chellappa. Robust height estimation of moving objects from uncalibrated videos. IEEE Transactions on Image Processing, vol. 19, no. 8, pp. 2221–2232, 2010. DOI:

    MathSciNet  Article  Google Scholar 

  10. [10]

    J. C. Liu, R. T. Collins, Y. X. Liu. Surveillance camera autocalibration based on pedestrian height distributions. In Proceedings of the British Machine Vision Conference, Dundee, UK, 2011.

    Google Scholar 

  11. [11]

    S. W. Park, T. E. Kim, J. S. Choi. Real-time estimation of trajectories and heights of pedestrians. In Proceedings of International Conference on Information Science and Applications, IEEE, Jeju Island, South Korea, 2011. DOI:

    Google Scholar 

  12. [12]

    D. Xu, H. W. Wang, Y. F. Li, M. Tan. A new calibration method for an inertial and visual sensing system. International Journal of Automation and Computing, vol. 9, no. 3, pp. 299–305, 2012. DOI:

    Article  Google Scholar 

  13. [13]

    H. J. Song, Y. Z. Chen, Y. Y. Gao. Velocity calculation by automatic camera calibration based on homogenous fog weather condition. International Journal of Automation and Computing, vol. 10, no. 2, pp. 143–156, 2013. DOI:

    Article  Google Scholar 

  14. [14]

    F. A. Andaló, G. Taubin, S. Goldenstein. Efficient height measurements in single images based on the detection of vanishing points. Computer Vision and Image Understanding, vol. 138, no. 2, pp. 51–60, 2015. DOI:

    Article  Google Scholar 

  15. [15]

    J. Jung, H. Kim, I. Yoon, J. Paik. Human height analysis using multiple uncalibrated cameras. In Proceedings of IEEE International Conference on Consumer Electronics, IEEE, Las Vegas, USA, pp. 213–214, 2016. DOI:

    Google Scholar 

  16. [16]

    J. Jung, I. Yoon, S. Lee, J. Paik. Object detection and tracking-based camera calibration for normalized human height estimation. Journal of Sensors, vol. 2016, Article number 8347841, 2016. DOI:

    Article  Google Scholar 

  17. [17]

    L. Y. Xu, Z. Q. Cao, P. Zhao, C. Zhou. A new monocular vision measurement method to estimate 3D positions of objects on floor. International Journal of Automation and Computing, vol. 14, no. 2, pp. 159–168, 2017. DOI:

    Article  Google Scholar 

  18. [18]

    J. W. Li, W. Gao, Y. H. Wu. Elaborate scene reconstruction with a consumer depth camera. International Journal of Automation and Computing, vol. 15, no. 4, pp. 443–453, 2018. DOI:

    Article  Google Scholar 

  19. [19]

    B. X. Wu, S. U. Ay, A. Abdel-Rahim. Trapezoid pixel array complementary metal oxide semiconductor image sensor with simplified mapping method for traffic monitoring applications. Optical Engineering, vol. 57, no. 9, Article number 093106, 2018. DOI:

    Google Scholar 

  20. [20]

    B. X. Wu, A. Abdel-Rahim, S. U. Ay. A trapezoid CMOS image sensor with 2% detection accuracy for traffic monitoring. In Proceedings of the 60th International Midwest Symposium on Circuits and Systems, IEEE, Boston, USA, pp. 1154–1158, 2017. DOI:

    Google Scholar 

  21. [21]

    F. Rameau, A. Habed, C. Demonceaux, D. Sidibé, D. Fofi. Self-calibration of a PTZ camera using new LMI constraints. In Proceedings of the 11th Asian Conference on Computer Vision, Springer, Daejeon, Korea, pp. 297–308, 2012. DOI:

    Google Scholar 

  22. [22]

    Y. T. Li, J. Zhang, W. W. Hu, J. W. Tian. Method for pantilt camera calibration using single control point. Journal of the Optical Society of America A, vol. 32, no. 1, pp. 156–163, 2015. DOI:

    Article  Google Scholar 

  23. [23]

    J. Nakamura. Image Sensors and Signal Processing for Digital Still Cameras, Boca Raton, USA: Taylor & Francis Group, 2006.

    Google Scholar 

  24. [24]

    L. A. Klein, M. K. Mills, D. R. P. Gibson. Traffic Detector Handbook, Volume II, 3rd ed, FHWA-HRT-06-139, USDOT, Washington, USA, 2006.

    Google Scholar 

  25. [25]

    A. Elgammal, R. Duraiswami, D. Harwood, L. S. Davis. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE, vol. 90, no. 7, pp. 1151–1163, 2002. DOI:

    Article  Google Scholar 

  26. [26]

    N. Kanopoulos, N. Vasanthavada, R. L. Baker. Design of an image edge detection filter using the Sobel operator. IEEE Journal of Solid-state Circuits, vol. 23, no. 2, pp. 358–367, 1988. DOI:

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Suat Utku Ay.

Additional information

Recommended by Associate Editor De Xu

Bing-Xing Wu received the B. Eng. degree in electrical engineering from Shanghai University of Electric Power, China in 2012, the M. Eng. and Ph. D. degrees in electrical engineering from University of Idaho, USA in 2014 and 2018, respectively. He is currently a lecturer and electric microscope expert in Department of Electrical and Computer Engineering, University of Idaho, USA.

His research interests include CMOS image sensor design, digital image processing, machine vision, and vision based traffic detection.

Suat Utku Ay received the M. Sc. and Ph. D. degrees in electrical engineering from the University of Southern California (USC), USA in 1997 and 2005, respectively. His Ph. D. thesis involved designing large format scientific CMOS image sensors for space applications. From September 1997 to July 2007, he was working in the industry as VLSI design engineer specializing in the area of mixed-signal very large scale integration (VLSI) design and CMOS image sensors. He was with Photobit Corporation which later became the Micron Technology Inc.′s Imaging Division in 2001 and Aptina Imaging in 2008 and On Semiconductor in 2015. He joined the Department of Electrical and Computer Engineering, University of Idaho, USA, on August 2007 as an assistant professor and become an associate professor in 2013. He is a member of the IEEE Solid State Circuits, IEEE Circuits and Systems, IEEE Electron Devices, and Society of Photo-optical Instrumentation (SPIE) societies.

His research interests include VLSI analog and mixed-signal integrated circuit (IC) design techniques for new class of baseband and radio frequency (RF) circuits and systems, on intelligent sensor systems with emphasis of reconfigurable, secure, flexible electro-optical circuit and devices, and on self-sustained and smart CMOS sensors for remote wireless network and systems.

Ahmed Abdel-Rahim is a professor in the Civil & Environmental Engineering Department at the University of Idaho, USA and the director of the University′s National Institute for Advanced Transportation Technology (NIATT), USA. He earned his doctorate in transportation engineering from Michigan State University, USA in 1998. He has published more than 50 refereed publications and has over 20 years of experience managing research projects. He is the lead principal investigator and the director of the University of Idaho′s University Transportation Center, USA that focuses on Transportation for Livability by Integrating the Vehicle and the Environment (TranLIVE).

His research interests include connected vehicle applications, modeling the environmental impact of vehicle operations, intelligent transportation systems (ITS), traffic operations and control technology, traffic modeling, security and survivability of transportation networks, and highway traffic safety.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, BX., Ay, S.U. & Abdel-Rahim, A. Pedestrian Height Estimation and 3D Reconstruction Using Pixel-resolution Mapping Method Without Special Patterns. Int. J. Autom. Comput. 16, 449–461 (2019).

Download citation


  • Traffic monitoring application
  • spatial resolution
  • pixel-resolution mapping (P-RM) method
  • 3D information
  • pedestrian height estimation