title: KERNEL-BASED HIGH-DIMENSIONAL HISTOGRAM ESTIMATION FOR VISUAL TRACKING.
abstract: We propose an approach for non-rigid tracking that represents objects by their set of distribution parameters. Compared to joint histogram representations, a set of parameters such as mixed moments provides a significantly reduced size representation. The discriminating power is comparable to that of the corresponding full high-dimensional histogram yet at far less spatial and computational complexity. The proposed method is robust in the presence of noise and illumination changes, and provides a natural extension to the use of mixture models. Experiments demonstrate that the proposed method outperforms both full color mean-shift and global covariance searches.
authors.affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia.
authors.forename: Peter
authors.initials: P
authors.lastname: Karasev
authors.forename: James
authors.initials: J
authors.lastname: Malcolm
authors.forename: Allen
authors.initials: A
authors.lastname: Tannenbaum
keywords: Object tracking; kernel density estimation; mean-shift; region covariance