Abstract
Automatic eigentemplate learning is discussed for a sparse template tracker. It is known that a sparse template tracker can effectively track a moving target using an eigentemplate when it is appropriately prepared for a motion class or for an illumination class. However, it has not been easy to prepare an eigentemplate automatically for any image sequences. This paper provides a feasible solution to this problem in the framework of sparse template tracking. In the learning phase, the sparse template tracker adaptively tracks a target object in a given image sequence when the first template is provided in the first image. By selecting a small number of representative and effective images, we can make up an eigentemplate by the principal component analysis. Once the eigentemplate learning is accomplished, the sparse template tracker can work with the eigentemplate instead of an adaptive template. Since the sparse eigentemplate tracker doesn't require any adaptive tracking, it can work more efficiently and effectively for image sequences in the class of learned appearance changes. Experimental results are provided for real-time face tracking when eigentemplates are learned for pose changes and for illumination changes, respectively.
| Original language | English |
|---|---|
| Pages (from-to) | 714-725 |
| Number of pages | 12 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 5414 LNCS |
| DOIs | |
| Publication status | Published - 2009 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Automatic eigentemplate learning for sparse template tracker. / Sakabe, Keiji; Taguchi, Tomoyuki; Shakunaga, Takeshi.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5414 LNCS, 2009, p. 714-725.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Automatic eigentemplate learning for sparse template tracker
AU - Sakabe, Keiji
AU - Taguchi, Tomoyuki
AU - Shakunaga, Takeshi
PY - 2009
Y1 - 2009
N2 - Automatic eigentemplate learning is discussed for a sparse template tracker. It is known that a sparse template tracker can effectively track a moving target using an eigentemplate when it is appropriately prepared for a motion class or for an illumination class. However, it has not been easy to prepare an eigentemplate automatically for any image sequences. This paper provides a feasible solution to this problem in the framework of sparse template tracking. In the learning phase, the sparse template tracker adaptively tracks a target object in a given image sequence when the first template is provided in the first image. By selecting a small number of representative and effective images, we can make up an eigentemplate by the principal component analysis. Once the eigentemplate learning is accomplished, the sparse template tracker can work with the eigentemplate instead of an adaptive template. Since the sparse eigentemplate tracker doesn't require any adaptive tracking, it can work more efficiently and effectively for image sequences in the class of learned appearance changes. Experimental results are provided for real-time face tracking when eigentemplates are learned for pose changes and for illumination changes, respectively.
AB - Automatic eigentemplate learning is discussed for a sparse template tracker. It is known that a sparse template tracker can effectively track a moving target using an eigentemplate when it is appropriately prepared for a motion class or for an illumination class. However, it has not been easy to prepare an eigentemplate automatically for any image sequences. This paper provides a feasible solution to this problem in the framework of sparse template tracking. In the learning phase, the sparse template tracker adaptively tracks a target object in a given image sequence when the first template is provided in the first image. By selecting a small number of representative and effective images, we can make up an eigentemplate by the principal component analysis. Once the eigentemplate learning is accomplished, the sparse template tracker can work with the eigentemplate instead of an adaptive template. Since the sparse eigentemplate tracker doesn't require any adaptive tracking, it can work more efficiently and effectively for image sequences in the class of learned appearance changes. Experimental results are provided for real-time face tracking when eigentemplates are learned for pose changes and for illumination changes, respectively.
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U2 - 10.1007/978-3-540-92957-4_62
DO - 10.1007/978-3-540-92957-4_62
M3 - Article
VL - 5414 LNCS
SP - 714
EP - 725
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -