Online Learning of Probabilistic Appearance Manifolds for Video-based Recognition and Tracking
Abstract
This research presents an online learning algorithm to
construct from video sequences an image-based representation that
is useful for recognition and tracking. For a class of objects
(e.g., human faces), a generic representation of the appearances
of the class is learned off-line. From video of an instance of
this class (e.g., a particular person), an appearance model is
incrementally learned on-line using the prior generic model and
successive frames from the video. More specifically, both the
generic and individual appearances are represented as an
appearance manifold that is approximated by a collection of
sub-manifolds (named pose manifolds) and the connectivity between
them. In turn, each sub-manifold is approximated by a
low-dimensional linear subspace while the connectivity is modeled
by transition probabilities between pairs of sub-manifolds. We
demonstrate that our online learning algorithm constructs an
effective representation for face tracking, and its use in
video-based face recognition compares favorably to the
representation constructed with a batch technique.
Online Learning of Probabilistic Appearance Manifolds for Video-based Recognition and Tracking [ pdf ]
Kuang-Chih Lee, David Kriegman / IEEE Conf. On Computer Vision and Pattern Recognition, 2005, oral presentation