Human Tracking Using Wigner Distribution and Rule-Based System in RGB Video

  • J. R. MahajanEmail author
  • C. S. Rawat
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)


In recent times, human tracking plays a crucial role in several applications like surveillance, free biometry, realistic world etc. In this research work, we suggest a new method to track the objects like humans using the motion obtained from color images. This algorithm does not use the object characteristics which is tracked and hence it resembles human eyes that uses the process of tracking in all the available images in RGB. Spatial and temporal association of motions are considered for motion association, which is the proposed plan of action to decrease the undesired selection process. Furthermore, for different images the Wigner distribution has been used which is less dependent on the fluctuation in threshold frame and thus reduces the untrue object detections. The results acquired with this algorithm is identical and consistent which in turn provides the reduction in computational complexity of this algorithm.


Human tracking Wigner distribution 


  1. 1.
    Wolff, L.B., Socolinsky, D.A., Eveland, C.K.: Chapter 6, Face recognition in the thermal infraredGoogle Scholar
  2. 2.
    Herrero, E., Orrite, C., Alcolea, A., Roy, A., Guerrero, J.J., Sagüés, C.: Video-sensor for detection and tracking of moving objects. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds.) IbPRIA, Pattern Recognition and Image Analysis. LNCS, vol. 2652, pp. 346–353. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Van Beek, P.J.L., Tekalp, A.M., Puri, A.: 2-D mesh geometry and motion compression for efficient object-based video representation. In: Proceedings of the International Conference on Image Processing, vol. 3, pp. 440–443 (1997)Google Scholar
  4. 4.
    Altunbasak, Y., Murat Tekalp, A., Bozdagi, G.: Two-dimensional object-based coding using a content-based mesh and affine motion parameterization. In: Proceedings of the International Conference on Image Processing, vol. 2, pp. 394–397 (1995)Google Scholar
  5. 5.
    Badawy, W., Bayoumi, M.: A mesh based motion tracking architecture. In: The 2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001, vol. 4, pp. 262–265 (2001)Google Scholar
  6. 6.
    Jain, J., Jain, A.: Displacement measurement and its application in interframe image coding. IEEE Trans. Commun. 29(12), 1799–1808 (1981)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Li, R., Zeng, B., Liou, M.L.: A new three-step search algorithm for block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 4(4), 438–442 (1994)CrossRefGoogle Scholar
  9. 9.
    Po, L.-M., Ma, W.-C.: A novel four-step search algorithm for fast block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 6(3), 313–317 (1996)CrossRefGoogle Scholar
  10. 10.
    Hsieh, H.-H., Lai, Y.-K.: A novel fast motion estimation algorithm using fixed subsampling pattern and multiple local winners search. In: The 2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001, vol. 2, pp. 241–244 (2001)Google Scholar
  11. 11.
    Srinivasan, R., Rao, K.: Predictive coding based on efficient motion estimation. IEEE Trans. Commun. 33(8), 888–896 (1985)CrossRefGoogle Scholar
  12. 12.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)CrossRefGoogle Scholar
  13. 13.
    Isard, M., Blake, A.: CONDENSATION—conditional density propagation for visual tracking. Int. J. Comput. Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  14. 14.
    Wigner, E.: On the quantum correction of thermodynamic equibilibrium. Phys. Rev. 40, 749–759 (1932)CrossRefGoogle Scholar
  15. 15.
    Padole, C.N., Vaidya, V.G.: Image restoration using Wigner distribution for night vision system. In: 9th International Conference on Signal Processing, ICSP 2008, pp. 844–848 (2008)Google Scholar
  16. 16.
    Vaidya, V.G., Padole, C.N.: Night vision enhancement using Wigner distribution. In: 3rd International Symposium on Communications, Control and Signal Processing, ISCCSP 2008, pp. 1268–1272 (2008)Google Scholar
  17. 17.
  18. 18.
    Padole, C.N., Alexandre, L.A.: Wigner distribution based motion tracking of human beings using thermal imaging. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, San Francisco, CA, pp. 9–14 (2010)Google Scholar

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Authors and Affiliations

  1. 1.Department of ETEPacific UniversityUdaipurIndia
  2. 2.Department of ETEVivekanand Institute of TechnologyMumbaiIndia

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