• DocumentCode
    2325556
  • Title

    A method for tracking the pose of known 3-D objects based on an active contour model

  • Author

    Stark, Katrin ; Fuchs, Siegfried

  • Author_Institution
    Dept. of Comput. Sci., Tech. Univ. Dresden, Germany
  • Volume
    1
  • fYear
    1996
  • fDate
    25-29 Aug 1996
  • Firstpage
    905
  • Abstract
    A method for tracking the pose of known 3-D objects moving with 6 degrees of freedom in space is described. The main idea is to split the tracking into two quasi autonomous working processes, one to track the 2-D outline (i.e. silhouette) of the object in an image sequence and one to track the full 3-D object pose based on the estimated 2-D outline. An active contour model utilizing a Kalman filter is used to efficiently track the object outline. Geometric information used is limited to the space curve at the object´s surface projecting to the currently visible outline. The 3-D pose tracker uses the results of the contour tracker, i.e. the position and shape of the outline, and a 3-D object model to track the full object pose. The object pose is derived from an n-point correspondence between the outline and the object´s surface. Furthermore, the pose tracker predicts the object appearance and provides the contour tracker with new model information in case the object aspect changes. Tracking results for polyhedral objects are presented
  • Keywords
    Kalman filters; active vision; computer vision; filtering theory; image sequences; motion estimation; tracking; 2D outline; 3D objects; 3D pose tracker; Kalman filter; active contour model; correspondence; geometric information; image sequence; polyhedral objects; pose tracking; silhouette; space curve; Active contours; Cameras; Computer science; Geometry; Image sequences; Kalman filters; Predictive models; Shape; Solids; Tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 1996., Proceedings of the 13th International Conference on
  • Conference_Location
    Vienna
  • ISSN
    1051-4651
  • Print_ISBN
    0-8186-7282-X
  • Type

    conf

  • DOI
    10.1109/ICPR.1996.546155
  • Filename
    546155