• DocumentCode
    2917090
  • Title

    Fast video analysis by genetic programming

  • Author

    Song, Andy

  • Author_Institution
    Sch. of Comput. Sci. & Inf. Technol., RMIT Univ., Melbourne, VIC
  • fYear
    2008
  • fDate
    1-6 June 2008
  • Firstpage
    3237
  • Lastpage
    3243
  • Abstract
    Genetic programming has been applied to various types of vision tasks. This paper extends the use of this powerful problem solving method to a more complex but more common domain, video analysis. We present the methodology as well as the experiments on two video analysis tasks: segmenting texture regions and detecting moving objects. The advantages of GP in this domain can be shown by this study. Firstly GP methods are less dependent on knowledge from domain experts. One methodology is suitable for both tasks. Secondly GP can generate fast video frame analyzers which are highly desirable or even critical in real time vision applications.
  • Keywords
    genetic algorithms; image segmentation; image texture; object detection; video signal processing; fast video analysis; genetic programming; moving object detection; texture region segmentation; vision tasks; Data mining; Delay; Face detection; Genetic programming; Information analysis; Mobile handsets; Motion detection; Object detection; Problem-solving; Streaming media;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-1822-0
  • Electronic_ISBN
    978-1-4244-1823-7
  • Type

    conf

  • DOI
    10.1109/CEC.2008.4631236
  • Filename
    4631236