Optical Flow-Based Tracking of Deformable Objects Using a Non-prior Training Active Feature Model

This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure ca...

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Published inAdvances in Multimedia Information Processing - PCM 2004 pp. 69 - 78
Main Authors Kim, Sangjin, Kang, Jinyoung, Shin, Jeongho, Lee, Seongwon, Paik, Joonki, Kang, Sangkyu, Abidi, Besma, Abidi, Mongi
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
SeriesLecture Notes in Computer Science
Online AccessGet full text
ISBN9783540239857
3540239855
ISSN0302-9743
1611-3349
DOI10.1007/978-3-540-30543-9_10

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Abstract This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time.
AbstractList This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time.
Author Kang, Jinyoung
Abidi, Mongi
Lee, Seongwon
Kim, Sangjin
Shin, Jeongho
Kang, Sangkyu
Paik, Joonki
Abidi, Besma
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Satoh, Shin’ichi
Aizawa, Kiyoharu
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Notes This work was supported by Korean Ministry of Science and Technology under the National Research Lab. Project, by Korean Ministry of Education under Brain Korea 21 Project, by the University Research Program in Robotics under grant DOE-R01-1344148, by the DOD/TACOM/NAC/ARC Program R01-1344-18, and by FAA/NSSA Program, R01-1344-48/49.
This revised version was published online in November 2004. Part of the main title was omitted in the printed version and the original online version of this paper.
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PublicationSeriesTitle Lecture Notes in Computer Science
PublicationSubtitle 5th Pacific Rim Conference on Multimedia, Tokyo, Japan, November 30 - December 3, 2004. Proceedings, Part III
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Snippet This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed...
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Title Optical Flow-Based Tracking of Deformable Objects Using a Non-prior Training Active Feature Model
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