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2012 | Vol. 5, iss. 4 | 17-20
Tytuł artykułu

Automatic estimation of the brightness changes for background suppression methods used for video tracking of vehicles

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the typical distortions in the background estimation methods is a change of lighting conditions, since each such change influences on the luminance of pixels in the captured images, which may be classified as the background. The global changes are relatively easy to compensate, but in practical applications the character of most of such changes is rather local. These changes may be caused e.g. by clouds, moving large objects, street lamps etc. Nevertheless, their influence on the results of the background estimation should be reduced therefore a local adaptive correction algorithm, applied as the pre-processing step, is proposed in the paper, assuming known geometrical configuration of the observed road
Wydawca

Rocznik
Strony
17-20
Opis fizyczny
Bibliogr. 12 poz.
Twórcy
autor
autor
  • Faculty of Motor Transport, Higher School of Technology and Economics in Szczecin, Klonowica 14, 71-244 Szczecin, Poland, okarma@wste.szczecin.pl
Bibliografia
  • [1] KLEIN L.A.: Sensor Technologies and Data Requirements for ITS. Artech House ITS library, Norwood, Massachusetts, 2001.
  • [2] PICCARDI M.: Background subtraction techniques: a review. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, The Hague, Netherlands, pp. 3099–3104, October 2004.
  • [3] REDDY V., SANDERSON C., LOVELL B.C.: A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts. EURASIP Journal on Image and Video Processing, Article ID 164956, 2011.
  • [4] OKARMA K., MAZUREK P.: Nonlinear background estimation methods for video vehicle tracking systems. Archives of Transport Systems Telematics, vol. 4, no. 4, p. 42–48, Katowice 2011.
  • [5] OKARMA K., MAZUREK P.: A modified hybrid method of nonlinear background estimation for vision based vehicle tracking systems. Logistyka (Logistics), no. 4, p. 1747–1752, 2012 (in Polish).
  • [6] OKARMA K., MAZUREK P.: Application of shape analysis techniques for the classification of vehicles. Communications in Computer and Information Science, vol. 104, p. 218–225, Springer Heidelberg, 2010.
  • [7] MAZUREK P., OKARMA K.: Application of Bayesian a priori distributions for vehicles’ video tracking systems. Communications in Computer and Information Science, vol. 104, p. 347–355, Springer Heidelberg, 2010.
  • [8] OKARMA K., MAZUREK P.: Vehicle tracking using the high dynamic range technology. Communications in Computer and Information Science, vol. 239, p. 172–179, Springer Heidelberg, 2011.
  • [9] BLACKMAN S.: Multiple-Target Tracking with Radar Applications. Artech House, Norwood, Massachusetts, 1986.
  • [10] BLACKMAN S., POPOLI R.: Design and Analysis of Modern Tracking Systems. Artech House, Norwood, Massachusetts, 1999.
  • [11] BOERS Y. et al.: Track before detect algorithm. EURASIP Journal on Advances in Signal Processing, Article ID 413932, 2008.
  • [12] STONE L., BARLOW C., CORWIN T.: Bayesian Multiple Target Tracking. Artech House, Norwood, Massachusetts, 1999.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL3-0026-0131
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