Spatiotemporal Approach for Tracking Using Rough Entropy and Frame Subtraction

  • B. Uma Shankar
  • Debarati Chakraborty
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

We present here an approach for video image segmentation where spatial segmentation is based on rough sets and granular computing and temporal segmentation is done by consecutive frame subtraction. Then the intersection of the temporal segmentation and spatial segmentation for the same frame is analyzed in RGB feature space. The estimated statistics of the intersecting regions is used for the object reconstruction and tracking.

Keywords

Segmentation rough entropy rough sets video tracking 

References

  1. 1.
    AVSS-2007: Fourth IEEE Int. Conf. Adv. Video & Signal Based Surveillance (2007)Google Scholar
  2. 2.
    Butenkov, S.A.: Granular computing in image processing and understanding. In: Proc. IASTED Int. Conf. Artificial Intelligence and Applns, pp. 811–816 (2004)Google Scholar
  3. 3.
    Chakraborty, D., Shankar, B.U.: Rough entropy based object segmenatation and tracking in video images. Tech. Rep. MIU/TR/-02/10, MIU, ISI (2010)Google Scholar
  4. 4.
    Hassanien, A.E., et al.: Rough sets and near sets in medical imaging: A review. IEEE Trans. on Information Technology in Biomedicine 13(6), 955–968 (2008)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Maggio, E., Cavallaro, A.: Video Tracking - Theory and Practice. Wiley, Chichester (2010)MATHGoogle Scholar
  6. 6.
    Pal, S.K., Peters, J.F. (eds.): Rough Fuzzy Image Analysis: Foundations and Methodologies. Chapman & Hall/CRC (2010)Google Scholar
  7. 7.
    Pal, S.K., Uma Shankar, B., Mitra, P.: Granular computing, rough entropy and object extraction. Pattern Recognition Letters 26(16), 2509–2517 (2005)CrossRefGoogle Scholar
  8. 8.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Norwell (1992)MATHGoogle Scholar
  9. 9.
    PETS-2000: IEEE Int. WS Perfor. Evaluation of Tracking and Surveillance (2000)Google Scholar
  10. 10.
    Pratt, W.K.: Digital Image Processing. John Wiley & Sons, New York (1991)MATHGoogle Scholar
  11. 11.
    Tekalp, A.M.: Digital Video Processing. Prentice Hall, New Jersey (1995)Google Scholar
  12. 12.
    Shankar, B.U.: Novel classification and segmentation techniques with application to remotely sensed images. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W.P. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 295–380. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), 1264–1291 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • B. Uma Shankar
    • 1
  • Debarati Chakraborty
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations