Tracking non-rigid objects using probabilistic Hausdorff distance matching

Sang Cheol Park, Sung Hoon Lim, Bong Kee Sin, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


This paper proposes a new method of extracting and tracking a non-rigid object moving against a cluttered background while allowing camera movement. For object extraction we first detect an object using watershed segmentation technique and then extract its contour points by approximating the boundary using the idea of feature point weighting. For object tracking we take the contour to estimate its motion in the next frame by the maximum likelihood method. The position of the object is estimated using a probabilistic Hausdorff measurement while the shape variation is modelled using a modified active contour model. The proposed method is highly tolerant to occlusion. Unless an object is fully occluded during tracking, the result is stable and the method is robust enough for practical application.

Original languageEnglish
Pages (from-to)2373-2384
Number of pages12
JournalPattern Recognition
Issue number12
Publication statusPublished - 2005 Dec


  • Active contour
  • Hausdorff distance
  • Object tracking
  • Watershed segmentation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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