Real-Time Kernel-Based Tracking in Joint Feature-Spatial Spaces

An object tracking algorithm that uses a novel simple symmetric similarityfunction between spatially-smoothed kernel-density estimates of the model
and target distributions is proposed and tested. 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
maximizing this similarity function. To alleviate the quadratic complexity
of the density estimation, we employ Gaussian kernels and the fast Gauss
transform to reduce the computations to linear order. This leads to a very
efficient and robust nonparametric tracking algorithm. The proposed
algorithm is tested with several image sequences and shown to achieve robust
and reliable real-time tracking.
(UMIACS-TR-2004-12)