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)

    Abstract

    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
    Volume38
    Issue number12
    DOIs
    Publication statusPublished - 2005 Dec

    Bibliographical note

    Funding Information:
    This research was supported by the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Science and Technology of Korea.

    Copyright:
    Copyright 2008 Elsevier B.V., All rights reserved.

    Keywords

    • 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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