• DocumentCode
    2938081
  • Title

    A Fast and Sub-Pixel Detector for Grid-Like Target in Camera Calibration

  • Author

    Shen, Le-Jun ; Ke, Zhun-Yu

  • Author_Institution
    Inst. of Image & Graphics, Sichuan Univ., Chengdu, China
  • fYear
    2010
  • fDate
    19-21 June 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Sub-pixel detection of target points is the performance bottleneck in camera calibration. Traditional algorithms are computational expensive or low precision when we do camera calibration in sport video analysis. In this paper, we propose a new algorithm to detect the grid-like target (i.e. tennis court in TV broadcasting). It has 3 parts: (1) color histogram based interested point classifier making our method faster; (2) sub-pixel refinement by non-linear least squares method improving the accuracy; (3) extended line scan using interested point as the start/end point finding the final line parameters. Results indicate that our detector is faster (<;9ms), more accurate and requires less memory than Hough based algorithms if target is grid-like: "straight lines link together".
  • Keywords
    calibration; image classification; image colour analysis; least squares approximations; object detection; sport; video signal processing; camera calibration; color histogram based interested point classifier; extended line scan; grid-like target detection; nonlinear least squares method; performance bottleneck; sport video analysis; start-end point finding; sub-pixel detector; sub-pixel refinement; Algorithm design and analysis; Calibration; Cameras; Detection algorithms; Detectors; Grid computing; Histograms; Image edge detection; Least squares methods; Multimedia communication;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Photonics and Optoelectronic (SOPO), 2010 Symposium on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-4963-7
  • Electronic_ISBN
    978-1-4244-4964-4
  • Type

    conf

  • DOI
    10.1109/SOPO.2010.5504245
  • Filename
    5504245