Object tracking with probabilistic hausdorff distance matching

Sang Cheol Park; Seong Whan Lee

doi:10.1007/11538059_25

Object tracking with probabilistic hausdorff distance matching

Sang Cheol Park, Seong Whan Lee

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

This paper proposes a new method of extracting and tracking a nonrigid object moving 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. Because the tracking result is stable unless an object is fully occluded during tracking, the proposed method can be applied to various applications.

Original language	English
Pages (from-to)	233-242
Number of pages	10
Journal	Lecture Notes in Computer Science
Volume	3644
Issue number	PART I
DOIs	https://doi.org/10.1007/11538059_25
Publication status	Published - 2005
Event	International Conference on Intelligent Computing, ICIC 2005 - Hefei, China Duration: 2005 Aug 23 → 2005 Aug 26

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 Commerce, Industry and Energy of Korea.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/11538059_25

Cite this

@article{8809392885114341a1ac0878e77ebd8c,

title = "Object tracking with probabilistic hausdorff distance matching",

abstract = "This paper proposes a new method of extracting and tracking a nonrigid object moving 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. Because the tracking result is stable unless an object is fully occluded during tracking, the proposed method can be applied to various applications.",

author = "Park, {Sang Cheol} and Lee, {Seong Whan}",

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 Commerce, Industry and Energy of Korea.; International Conference on Intelligent Computing, ICIC 2005 ; Conference date: 23-08-2005 Through 26-08-2005",

year = "2005",

doi = "10.1007/11538059_25",

language = "English",

volume = "3644",

pages = "233--242",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

number = "PART I",

}

TY - JOUR

T1 - Object tracking with probabilistic hausdorff distance matching

AU - Park, Sang Cheol

AU - Lee, Seong Whan

N1 - 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 Commerce, Industry and Energy of Korea.

PY - 2005

Y1 - 2005

N2 - This paper proposes a new method of extracting and tracking a nonrigid object moving 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. Because the tracking result is stable unless an object is fully occluded during tracking, the proposed method can be applied to various applications.

AB - This paper proposes a new method of extracting and tracking a nonrigid object moving 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. Because the tracking result is stable unless an object is fully occluded during tracking, the proposed method can be applied to various applications.

UR - http://www.scopus.com/inward/record.url?scp=27144443092&partnerID=8YFLogxK

U2 - 10.1007/11538059_25

DO - 10.1007/11538059_25

M3 - Conference article

AN - SCOPUS:27144443092

SN - 0302-9743

VL - 3644

SP - 233

EP - 242

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

IS - PART I

T2 - International Conference on Intelligent Computing, ICIC 2005

Y2 - 23 August 2005 through 26 August 2005

ER -

Object tracking with probabilistic hausdorff distance matching

Abstract

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this