Optical flow-based tracking of deformable objects using a non-prior training active feature model
- Authors
- Kim, S.; Kang, J.; Shin, J.; Lee, S.; Paik, J.; Kang, S.; Abidi, B.; Abidi, M.
- Issue Date
- Dec-2004
- Publisher
- Springer Verlag
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.3333 LNCS, pp 69 - 78
- Pages
- 10
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 3333 LNCS
- Start Page
- 69
- End Page
- 78
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56300
- DOI
- 10.1007/978-3-540-30543-9_10
- ISSN
- 0302-9743
1611-3349
- 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. © Springer-Verlag Berlin Heidelberg 2004.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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