Abstract
Automatic eigentemplate learning is discussed for a sparse template tracker. Using an eigentemplate learned from multiple sequences, a sparse template tracker can efficiently track a target that changes appearance. The present paper provides a feasible solution for eigentemplate learning when multiple image sequences are available. Two types of eigentemplates are compared in the present paper, namely, a single eigentemplate, and a set of directional eigentemplates. The single eigentemplate simply consists of all images learned from multiple sequences.On the other hand, directional eigentemplates are obtained by decomposing the single eigentemplate into three directions of the face poses. The sparse template tracker is also expanded to directional eigentemplates.Finally, the effectiveness of the provided solution is demonstrated in the learning and tracking experiments. The experimental results indicate that directional learning works well with small seed data,and that the directional eigentracker works better than the single eigentracker.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 104-115 |
| Number of pages | 12 |
| Volume | 7088 LNCS |
| Edition | PART 2 |
| DOIs | |
| Publication status | Published - 2011 |
| Event | 5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011 - Gwangju, Korea, Republic of Duration: Nov 20 2011 → Nov 23 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 2 |
| Volume | 7088 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011 |
|---|---|
| Country | Korea, Republic of |
| City | Gwangju |
| Period | 11/20/11 → 11/23/11 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Directional eigentemplate learning for sparse template tracker. / Seto, Hiroyuki; Taguchi, Tomoyuki; Shakunaga, Takeshi.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7088 LNCS PART 2. ed. 2011. p. 104-115 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7088 LNCS, No. PART 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Directional eigentemplate learning for sparse template tracker
AU - Seto, Hiroyuki
AU - Taguchi, Tomoyuki
AU - Shakunaga, Takeshi
PY - 2011
Y1 - 2011
N2 - Automatic eigentemplate learning is discussed for a sparse template tracker. Using an eigentemplate learned from multiple sequences, a sparse template tracker can efficiently track a target that changes appearance. The present paper provides a feasible solution for eigentemplate learning when multiple image sequences are available. Two types of eigentemplates are compared in the present paper, namely, a single eigentemplate, and a set of directional eigentemplates. The single eigentemplate simply consists of all images learned from multiple sequences.On the other hand, directional eigentemplates are obtained by decomposing the single eigentemplate into three directions of the face poses. The sparse template tracker is also expanded to directional eigentemplates.Finally, the effectiveness of the provided solution is demonstrated in the learning and tracking experiments. The experimental results indicate that directional learning works well with small seed data,and that the directional eigentracker works better than the single eigentracker.
AB - Automatic eigentemplate learning is discussed for a sparse template tracker. Using an eigentemplate learned from multiple sequences, a sparse template tracker can efficiently track a target that changes appearance. The present paper provides a feasible solution for eigentemplate learning when multiple image sequences are available. Two types of eigentemplates are compared in the present paper, namely, a single eigentemplate, and a set of directional eigentemplates. The single eigentemplate simply consists of all images learned from multiple sequences.On the other hand, directional eigentemplates are obtained by decomposing the single eigentemplate into three directions of the face poses. The sparse template tracker is also expanded to directional eigentemplates.Finally, the effectiveness of the provided solution is demonstrated in the learning and tracking experiments. The experimental results indicate that directional learning works well with small seed data,and that the directional eigentracker works better than the single eigentracker.
UR - http://www.scopus.com/inward/record.url?scp=82155185340&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82155185340&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25346-1_10
DO - 10.1007/978-3-642-25346-1_10
M3 - Conference contribution
AN - SCOPUS:82155185340
SN - 9783642253454
VL - 7088 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 115
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -