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 in | Advances in Multimedia Information Processing - PCM 2004 pp. 69 - 78 |
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| Main Authors | , , , , , , , |
| Format | Book Chapter |
| Language | English |
| Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2005
|
| Series | Lecture Notes in Computer Science |
| Online Access | Get full text |
| ISBN | 9783540239857 3540239855 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-540-30543-9_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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Sangjin surname: Kim fullname: Kim, Sangjin organization: Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea – sequence: 2 givenname: Jinyoung surname: Kang fullname: Kang, Jinyoung organization: Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea – sequence: 3 givenname: Jeongho surname: Shin fullname: Shin, Jeongho organization: Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea – sequence: 4 givenname: Seongwon surname: Lee fullname: Lee, Seongwon organization: Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea – sequence: 5 givenname: Joonki surname: Paik fullname: Paik, Joonki email: paikj@cau.ac.kr organization: Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea – sequence: 6 givenname: Sangkyu surname: Kang fullname: Kang, Sangkyu organization: Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, Knoxville, USA – sequence: 7 givenname: Besma surname: Abidi fullname: Abidi, Besma organization: Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, Knoxville, USA – sequence: 8 givenname: Mongi surname: Abidi fullname: Abidi, Mongi organization: Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, Knoxville, USA |
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| Copyright | Springer-Verlag Berlin Heidelberg 2004 |
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| DOI | 10.1007/978-3-540-30543-9_10 |
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| Editor | Nakamura, Yuichi 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 |
| PublicationTitle | Advances in Multimedia Information Processing - PCM 2004 |
| PublicationYear | 2005 |
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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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