3D Human Motion Reconstruction Using Video Processing

  • Nadiya Roodsarabi
  • Alireza Behrad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)


One of the important problems in human motion analysis is the 3D reconstruction of human motion, which utilizes the anatomic point’s positions. These points can uniquely define the position and orientation of all anatomical segments. In this paper, a new method for reconstruction of human motion from the image sequence of a single static camera is described. In this method 2D tracking is used for 3D reconstruction, which a database of selected frames are used for the correction of tracking process. We use Discrete Cosine Transform (DCT) block as a “Matrix des criptor” used in the matching process for finding appropriate frame in the database and tracking process. Finally, 3D reconstruction is performed using Taylor method. By using DCT, we can select best frequency region for various tasks such as tracking, matching, correcting joints and so on. Experimental results showed the promise of the algorithm.


Discrete Cosine Transform (DCT) Human motion reconstruction Video processing Occluded limb Pose corresponding Tracking Matching 


  1. 1.
    Horiguchi: Body Line Scanner.The development of a new 3-D measurement and Reconstruction system. In: International Archives of Photogrammetry and Remote Sensing, vol. 32, pp. 421-429 (1998)Google Scholar
  2. 2.
    Barrón, C., Kakadiaris, I.A.: Estimating anthropometry and pose from single uncalibrated image. Computer Vision and Image Understanding 81(3), 269–284 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Sminchisescu, C., Triggs, B.: Kinematic Jump Processes for Monocular 3D Human Tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition (2003)Google Scholar
  4. 4.
    Chen, C., Zhuang, Y., Xiao, J.: Towards Robust 3D Reconstruction of Human Motion from Monocular Video. In: Pan, Z., Cheok, A.D., Haller, M., Lau, R.W.H., Saito, H., Liang, R. (eds.) ICAT 2006. LNCS, vol. 4282, pp. 594–603. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Loy, G., Eriksson, M., Sullivan, J., Carlsson, S.: Monocular 3D Reconstruction of Human Motion in Long Action Sequences. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 442–455. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Hilton, A., Beresford, D., Gentils, T., Smith, R., Sun, W., Illingworth, J.: Whole-body modelling of people from multiview images to populate virtual worlds. The Visual Computer, International Journal of Computer Graphics 16, 411–436 (2000)zbMATHCrossRefGoogle Scholar
  7. 7.
    D´Apuzzo, N., Plänkers, R., Fua, P., Gruen, A., Thalmann, D.: Modeling human bodies from video sequences. In: El-Hakim, S.F., Gruen, A. (eds.) SPIE. Videometrics VI, vol. 3461, pp. 36–47 (1999)Google Scholar
  8. 8.
    Plänkers, R., Fua, P.: Tracking and modeling people in video sequences. Computer Vision and Image Understanding 81(3), 285–302 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Mikić, I., Triverdi, M., Hunter, E., Cosman, P.: Articulated body posture estimation from multi-camera voxel data. In: IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, vol. I, pp. 455–460 (2001)Google Scholar
  10. 10.
    Cheung, K.M., Kanade, T., Bouguet, J.Y., Holler, M.: A real time system for robust 3D voxel reconstruction of human motions. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 714–720 (2000)Google Scholar
  11. 11.
    Iwasawa, S., Ohya, J., Takahashi, K., Sakaguchi, T., Ebihara, K., Morishima, S.: Human body postures from trinocular camera images. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 326–331 (2000)Google Scholar
  12. 12.
    Plänkers, R., Fua, P., D’Apuzzo, N.: Automated body modeling from video sequences. In: International Conf. of Computer Vision (Workshop on Modeling People), pp. 45–52 (1999)Google Scholar
  13. 13.
    Rosales, R.E., Sclaroff, S.: Learning and synthesizing human body motion and posture. In: IEEE International Conf. on Automatic Face and Gesture Recognition, pp. 506–511 (2000)Google Scholar
  14. 14.
    Park, M.J., Choi, M.G., Shin, S.Y.: Human motion reconstruction from inter-frame feature correspondences of a single video stream using a motion library. In: ACM SIGGRAPH Symposium on Computer Animation, pp. 113–120 (2002) Google Scholar
  15. 15.
    Mori, G., Malik, J.: Estimating human body configurations using shape context matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 666–680. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Park, M.J., Choi, M.G., Gawa, Y.S., Shin, S.Y.: Video-Guided Motion Synthesis Using Example Motions. ACM Transactions on Graphics 25(4) (2006)Google Scholar
  17. 17.
    Mori, G., Belongie, S., Malik, J.: Shape contexts enable efficient retrieval of similar shapes. In: Proc. IEEE Comput.Soc. Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 723–730 (2001)Google Scholar
  18. 18.
    Taylor, C.J.: Reconstruction of articulated objects from point correspondences in a single uncalibrated image. In: CVIU, vol. 80, pp. 349–363 (2000)Google Scholar
  19. 19.
    Remondino, F., Roditakis, A.: 3D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 100–107. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  20. 20.
    Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI, pp. 674–679 (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nadiya Roodsarabi
    • 1
  • Alireza Behrad
    • 1
  1. 1.Faculty of EngineeringShahed UniversityTehranIran

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