• DocumentCode
    2728052
  • Title

    An improved detection algorithm of moving vehicles based on computer vision

  • Author

    Cao, Jie ; Wang, Wei ; Liang, Yan

  • Author_Institution
    Coll. of Comput. & Commun., Lanzhou Univ. of Technol., Lanzhou, China
  • Volume
    4
  • fYear
    2009
  • fDate
    20-22 Nov. 2009
  • Firstpage
    20
  • Lastpage
    24
  • Abstract
    Moving vehicle detection based on computer vision is an important aspect in the application of Intelligent Transportation Systems (ITS). How to get the accurate parameters of the vehicle in real-time, especially in complicated background situation is very critical. In this paper an improved detection algorithm of moving vehicle is proposed. According to the characteristics of some present detection algorithms, the paper makes improvement to background subtraction and symmetric difference method respectively, and combines both of them. In the method of background difference, the selective statistics background updating algorithm is proposed. Add region-growing segmentation and the morphological filtering methods to the post-processing step. Through simulation experiment with the real traffic video image, the effect is obvious.
  • Keywords
    computer vision; traffic engineering computing; computer vision; intelligent transportation system; morphological filtering; moving vehicles; region-growing segmentation; selective statistics background updating algorithm; traffic video image; vehicle detection algorithm; Application software; Computer vision; Detection algorithms; Filtering; Image segmentation; Intelligent transportation systems; Intelligent vehicles; Statistics; Traffic control; Vehicle detection; background subtraction; computer vision; detection of moving vehicles; selective statistical background updating; symmetrical difference;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-4754-1
  • Electronic_ISBN
    978-1-4244-4738-1
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2009.5357732
  • Filename
    5357732