Title: An Invariant Representation for Matching Trajectories across uncalibrated video streams
Authors: Walter Nunziati, Stan Sclaroff, and Alberto Del Bimbo
Date: May 19, 2005
Abstract:
We introduce a view-point invariant representation of moving
object trajectories that can be used in video database applications. It
is assumed that trajectories lie on a surface that can be locally approximated
with a plane. Raw trajectory data is first locally?approximated
with a cubic spline via least squares fitting. For each sampled point of
the obtained curve, a projective invariant feature is computed using a
small number of points in its neighborhood. The resulting sequence of
invariant features computed along the entire trajectory forms the view?
invariant descriptor of the trajectory itself. Time parametrization has
been exploited to compute cross ratios without ambiguity due to point
ordering. Similarity between descriptors of different trajectories is measured
with a distance that takes into account the statistical properties of
the cross ratio, and its symmetry with respect to the point at infinity. In
experiments, an overall correct classification rate of about 95% has been
obtained on a dataset of 58 trajectories of players in soccer video, and
an overall correct classification rate of about 80% has been obtained on
matching partial segments of trajectories collected from two overlapping
views of outdoor scenes with moving people and cars.