id: 05500204
dt: a
an: 05500204
au: Sakabe, Keiji; Taguchi, Tomoyuki; Shakunaga, Takeshi
ti: Automatic eigentemplate learning for sparse template tracker.
so: Wada, Toshikazu (ed.) et al., Advances in image and video technology. Third
    Pacific Rim symposium, PSIVT 2009, Tokyo, Japan, January 13‒16, 2009.
    Proceedings. Berlin: Springer (ISBN 978-3-540-92956-7/pbk). Lecture
    Notes in Computer Science 5414, 714-725 (2008).
py: 2008
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-540-92957-4_62
ab: Summary: 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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