Advertisement

Enhancing Tracking Capabilities of KDE Background Subtraction-Based Algorithm Using Edge Histograms

  • Piotr KowaleczkoEmail author
  • Przemyslaw Rokita
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)

Abstract

The paper presents a method which allows to improve tracking abilities of conventional background subtraction-based algorithm. The presented algorithm which is a result of the studies is a hybrid method consisting of the Kernel Density Estimation (KDE) background subtraction tracking method and the Edge Histograms Displacement Calculation (EHDC) algorithm. Tracking ratios before and after merging with EHDC have been measured and presented. The paper also describes an algorithm eliminating cyclic changes in image’s intensities values, which have significant influence on the input data for the hybrid algorithm. The influence of moving-camera video specificity on the output data has been pointed out.

Keywords

Edge histogram Background subtraction Tracking KDE EHDC 

References

  1. 1.
    Rokita, P.: Fast tracking using edge histograms. SPIE. Real-Time Imaging II, vol. 3028, p. 91, 3 Apr 1997Google Scholar
  2. 2.
    Kowaleczko P., Rokita P.: Wykorzystanie algorytmow opartych na metodzie Edge Histograms do okreslenia parametrow ruchu kamer glowicy optoelektronicznej WH-1 (in Polish), KNTWE 2014 conference materials (2014)Google Scholar
  3. 3.
    Lewis, J.P.: Fast normalized cross-correlation. Vis. Interface 10(1), 120–123 (1995)Google Scholar
  4. 4.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking—a survey. Acm Comput. Surv. (CSUR) 38(4), 13 (2006)CrossRefGoogle Scholar
  5. 5.
    Benezeth Y., Jodoin P., Emile B., Laurent H., Rosenberger C.: Review and Evaluation of Commonly-Implemented Background Subtraction Algorithms. In: Pattern Recognition, ICPR 2008, 19th International Conference on Pattern Recognition (2008)Google Scholar
  6. 6.
    Piccardi, M.: Background subtraction techniques: a review. 2004 IEEE Int. Conf. Syst. Man Cybern. 4, 3099–3104 (2004)CrossRefGoogle Scholar
  7. 7.
    Elgammal A., Harwood D., Davis L.: Non-parametric model for background subtraction, ECCV (2000)Google Scholar
  8. 8.
    Babich, G.A., Camps, O.I.: Weighted Parzen windows for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 18(5), 567–570 (1996)CrossRefGoogle Scholar
  9. 9.
    Kovac J., Peer P., Solina F.: Eliminating the Influence of Non-Standard Illumination from Images, Technical Report, (2003)Google Scholar
  10. 10.
    OpenCV documentation, http://docs.opencv.org

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.C4ISR Systems Integration DivisionAir Force Institute of TechnologyWarsawPoland
  2. 2.Faculty of CyberneticsMilitary University of TechnologyWarsawPoland

Personalised recommendations