Directional eigentemplate learning for sparse template tracker

Hiroyuki Seto, Tomoyuki Taguchi, Takeshi Shakunaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 languageEnglish
Title of host publicationAdvances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings
Number of pages12
EditionPART 2
Publication statusPublished - 2011
Event5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011 - Gwangju, Korea, Republic of
Duration: Nov 20 2011Nov 23 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7088 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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