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PCM
2004
Springer

Using a Non-prior Training Active Feature Model

13 years 9 months ago
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 (NPTAFM) 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.
Sangjin Kim, Jinyoung Kang, Jeongho Shin, Seongwon
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where PCM
Authors Sangjin Kim, Jinyoung Kang, Jeongho Shin, Seongwon Lee, Joon Ki Paik, SangKyu Kang, Besma R. Abidi, Mongi A. Abidi
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