| Abstract | We will demonstrate an object tracking algorithm thatuses a novel simple symmetric similarity function between
spatially-smoothed kernel-density estimates of the model
and target distributions. The similarity measure is based on
the expectation of the density estimates over the model or
target images. The density is estimated using radial-basis
kernel functions which measure the affinity between points
and provide a better outlier rejection property. The mean-
shift algorithm is used to track objects by iteratively max-
imizing this similarity function. To alleviate the quadratic
complexity of the density estimation, we employ Gaussian
kernels and the fast Gauss transform to reduce the compu-
tations to linear order. This leads to a very efficient and
robust nonparametric tracking algorithm. More details can
be found in [2]. The system processes online video stream
on a P4 1.4GHz and achieves 30 frames per second using
an ordinary webcam.
|
