Tracking Humans Using Prior and Learned Representations of Shape and Appearance

Jongwoo Lim, David Kriegman
FGR 2004, pp 869-874

Tracking a moving person is challenging because a person's appearance
in images changes significantly due to articulation, viewpoint
changes, and lighting variation across a scene.  And different people
appear differently due to numerous factors such as body shape,
clothing, skin color, and hair.  In this paper, we introduce a
multi-cue tracking technique that uses prior information about the 2-D
image shape of people in general along with an appearance model that
is learned on-line for a specific individual.  Assuming a static
camera, the background is modeled and updated on-line.  Rather than
performing thresholding and blob detection during tracking, a
foreground probability map (FPM) is computed which indicates the
likelihood that a pixel is not the projection of the background.
Off-line, a shape model of walking people is estimated from the FPMs
computed from training sequences. During tracking, this generic prior
model of human shape is used for person detection and to initialize 
a tracking process.  As this prior model is very generic, a model of an
individual's appearance is learned on-line during the tracking.  As
the person is tracked through a sequence using both shape and
appearance, the appearance model is refined and multi-cue tracking
becomes more robust.