Object tracking with probabilistic Hausdorff distance matching
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, SC | - |
dc.contributor.author | Lee, SW | - |
dc.date.accessioned | 2021-09-09T08:40:10Z | - |
dc.date.available | 2021-09-09T08:40:10Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2005 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/123582 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.title | Object tracking with probabilistic Hausdorff distance matching | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, SW | - |
dc.identifier.wosid | 000232528800025 | - |
dc.identifier.bibliographicCitation | ADVANCES IN INTELLIGENT COMPUTING, PT 1, PROCEEDINGS, v.3644, pp.233 - 242 | - |
dc.relation.isPartOf | ADVANCES IN INTELLIGENT COMPUTING, PT 1, PROCEEDINGS | - |
dc.citation.title | ADVANCES IN INTELLIGENT COMPUTING, PT 1, PROCEEDINGS | - |
dc.citation.volume | 3644 | - |
dc.citation.startPage | 233 | - |
dc.citation.endPage | 242 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.