TY - GEN
T1 - Directional eigentemplate learning for sparse template tracker
AU - Seto, Hiroyuki
AU - Taguchi, Tomoyuki
AU - Shakunaga, Takeshi
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
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.
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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
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 115
BT - Advances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings
T2 - 5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011
Y2 - 20 November 2011 through 23 November 2011
ER -