Title: "An Invariant Representation for Matching Trajectories across Uncalibrated Video Streams"
Authors: Walter Nunziati (University of Florence), Stan Sclaroff, Alberto Del Bimbo (University of Florence)
Abstract:
We introduce a viewpoint 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 in nity. 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.