A Single-View Based Framework for Robust Estimation of Height and Position of Moving People

  • Seok-Han Lee
  • Jong-Soo Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)


In recent years, there has been increased interest in characterizing and extracting 3D information from 2D images for human tracking and identification. In this paper, we propose a single view-based framework for robust estimation of height and position. In the proposed method, 2D features of target object is back-projected into the 3D scene space where its coordinate system is given by a rectangular marker. Then the position and the height are estimated in the 3D space. In addition, geometric error caused by inaccurate projective mapping is corrected by using geometric constraints provided by the marker. The accuracy and the robustness of our technique are verified on the experimental results of several real video sequences from outdoor environments.


Video surveillance height estimation position estimation human tracking 


  1. 1.
    Leibowitz, D., Criminisi, A., Zisserman, A.: Creating Architectural Models from Images. In: Eurograpihcs 1999. 20th Annual Conference of the European Association for Computer Graphics, Mailand, Italy, vol. 18, pp. 39–50 (1999)Google Scholar
  2. 2.
    Criminisi, A., Reid, I., Zisserman, A.: Single View Metrology. International Journal of Computer Vision 40, 123–148 (2000)zbMATHCrossRefGoogle Scholar
  3. 3.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge Univ. Press, Cambridge (2003)Google Scholar
  4. 4.
    Faugeras, O.: Three-Dimensional Computer Vision. The MIT Press, Cambridge (1993)Google Scholar
  5. 5.
    Criminisi, A.: Accurate Visual Metrology from Single and Multiple uncalibrated Images. Springer, London (2001)zbMATHGoogle Scholar
  6. 6.
    Golub, G., Loan, C.: Matrix Computations, 3rd edn. Johns Hopkins Univ. Press, Baltimore (1996)zbMATHGoogle Scholar
  7. 7.
    Zhang, Z.: Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 1330–1334 (2000)CrossRefGoogle Scholar
  8. 8.
    Hartley, R., Kang, S.: Parameter-free Radial Distortion Correction with Center of Distortion Estimation. In: Nicu, S., Michael S.L., Thomas S.H. (eds.): Proceedings of the Tenth IEEE International Conference on Computer Vision, ICCV 2005, Beijing, China, vol. 2, pp. 1834 – 1841 (2005)Google Scholar
  9. 9.
    Elgammel, A., Harwood, D., Davis, L.: Non-parametric model for back ground subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 751–767. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    BenAbdelkader, R., Cutler, D.L.: Person Identification using Automatic Height and Stride Estimation: In Anders, H. In: Anders, H., Gunnar, S., Mads, N., Peter, J. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 155–158. Springer, Heidelberg (2002)Google Scholar
  11. 11.
    Havasi, L., Szlák, Z., Szirányi, T.: Detection of Gait Characteristics for Scene Registration in Video Surveillance System. IEEE Transactions on Image Processing 16, 503–510 (2007)CrossRefGoogle Scholar
  12. 12.
    Liu, Z., Sarkar, S.: Improved Gait Recognition by Gait Dynamics Normalization. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 863–876 (2006)CrossRefGoogle Scholar
  13. 13.
    Lee, L., Romano, R., Stein, G.: Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 758–769 (2000)CrossRefGoogle Scholar
  14. 14.
    Hu, W., Hu, M., Zhou, X., Tan, T., Lou, J., Maybank, S.: Principal Axis-Based Correspondence between Multiple Cameras for People Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 663–671 (2000)Google Scholar
  15. 15.
    Kim, K., Davis, L.: Multi-camera Tracking and Segmentation of Occluded People on Ground Plane Using Search-Guided Particle Filtering. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 98–109. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Khan, S., Shah, M.: A Multiple View Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint. In: Ales, L., Horst, B., Axel, P. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 133–146. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Khan, S., Shah, M.: Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1355–1361 (2003)CrossRefGoogle Scholar
  18. 18.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 334–353 (2004)Google Scholar
  19. 19.
    Haritaoglu, I., Harwood, D., Davis, L.: W4: Real-time Surveillance of People. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 809–830 (2000)CrossRefGoogle Scholar
  20. 20.
    Mckenna, S., Jabri, S., Duric, J., Wechsler, H., Rosenfeld, A.: Tracking Groups of People. Computer Vision and Image Understanding 80, 42–56 (2000)zbMATHCrossRefGoogle Scholar
  21. 21.
    Gomez, J., Simon, G., Berger, M.: Calibration Errors in Augmented Reality: a Practical Study. In: ISMAR 2005. Fourth IEEE and ACM international Symposium on Mixed and Augmented Reality, Vienna, Austria, pp. 154–163 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Seok-Han Lee
    • 1
  • Jong-Soo Choi
    • 1
  1. 1.Dept. of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, 221 Huksuk-Dong, Dongjak-Ku, 156-756, SeoulKorea

Personalised recommendations