• DocumentCode
    2063
  • Title

    Significant Body Point Labeling and Tracking

  • Author

    Azhar, Faisal ; Tjahjadi, Tardi

  • Author_Institution
    Sch. of Eng., Univ. of Warwick, Coventry, UK
  • Volume
    44
  • Issue
    9
  • fYear
    2014
  • fDate
    Sept. 2014
  • Firstpage
    1673
  • Lastpage
    1685
  • Abstract
    In this paper, a method is presented to label and track anatomical landmarks (e.g., head, hand/arm, feet), which are referred to as significant body points (SBPs), using implicit body models. By considering the human body as an inverted pendulum model, ellipse fitting and contour moments are applied to classify it as being in Stand, Sit, or Lie posture. A convex hull of the silhouette contour is used to determine the locations of SBPs. The particle filter or a motion flow-based method is used to predict SBPs in occlusion. Stick figures of various activities are generated by connecting the SBPs. The qualitative and quantitative evaluation show that the proposed method robustly labels and tracks SBPs in various activities of two different (low and high) resolution data sets.
  • Keywords
    image motion analysis; object tracking; particle filtering (numerical methods); SBP; anatomical landmarks; contour moments; ellipse fitting; implicit body models; inverted pendulum model; lie posture; motion flow-based method; occlusion; particle filter; qualitative evaluation; quantitative evaluation; significant body point labeling; significant body point tracking; silhouette contour; sit posture; stand posture; Biological system modeling; Computational modeling; Knee; Labeling; Solid modeling; Torso; Tracking; Anthropometry; convex points; implicit body model; significant body points; stick figure;
  • fLanguage
    English
  • Journal_Title
    Cybernetics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2168-2267
  • Type

    jour

  • DOI
    10.1109/TCYB.2014.2303993
  • Filename
    6747306