• DocumentCode
    1686436
  • Title

    Object kinematic model: A novel approach of adaptive background mixture models for video segmentation

  • Author

    Yu, Jin ; Zhou, Xuan ; Qian, Feng

  • Author_Institution
    Key Lab. of Adv. Control & Optimization for Chem. Processes, East China Univ. of Sci. & Technol., Shanghai, China
  • fYear
    2010
  • Firstpage
    6225
  • Lastpage
    6228
  • Abstract
    In the field of video surveillance, adaptive Gaussian mixture model (GMM) is widely used as the background-pixel dynamic modeling approach. GMM produced each pixel Gaussian distribution corresponds to the respective, but this ignores the impact of the movement of the object itself. The ideas of object kinematic model is presented to guide the number of distribution in the process of iterative, which can speed up the process of clustering, and the results indicate that this method can improve the efficiency and stability of background modeling.
  • Keywords
    Gaussian distribution; image resolution; image segmentation; iterative methods; video surveillance; Gaussian distribution; adaptive background mixture models; object kinematic model; video segmentation; video surveillance; Adaptation model; Analytical models; Gaussian distribution; Hidden Markov models; Kinematics; Pixel; Trajectory; adaptive GMM; background modelling; background subtraction; object kinematic model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2010 8th World Congress on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-1-4244-6712-9
  • Type

    conf

  • DOI
    10.1109/WCICA.2010.5554402
  • Filename
    5554402