Tracking non-rigid objects using probabilistic Hausdorff distance matching

Authors:
Sang-Cheol Park;Sung-Hoon Lim;Bong-Kee Sin;Seong-Whan Lee
Affiliations:
Department of Computer Science and Engineering, Center for Artificial Vision Research, Korea University, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea;Department of Computer Science and Engineering, Center for Artificial Vision Research, Korea University, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea;Division of Electronics, Computer and Telematics Engineering, Pukyong National University, Taeyon-dong, Nam-ku, Busan 608-737, Korea;Department of Computer Science and Engineering, Center for Artificial Vision Research, Korea University, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea
Venue:
Pattern Recognition
Year:
2005

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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.